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By G. Vak. Gordon-Conwell Theological Seminary.

If this becomes possible generic cialis professional 20mg free shipping, flow cytometry will be the tool hospitals use to find stem cells in the blood 20mg cialis professional free shipping. These cells will be cultured and redirected to therapeutic levels for treating diseases like Parkinson’s buy cialis professional 40 mg free shipping, diabetes buy 20mg cialis professional otc, or spinal cord injury. Because they are cultured from an individual’s own cells, the recipient will not require a lifetime of immune suppressants to enable them to do their work. In all cases, the signals are detected by digital arrays and converted to digital information structured and stored by computers. These technologies, revolutionary when they were developed, made noninvasive evaluation of tissues and internal organs possi- ble, tilting diagnosis decisively away from exploratory surgery (and tilting power and clinical influence toward radiology). These images can reveal the extent of damage to the heart or brain from a heart attack or stroke and help determine if a tumor has been destroyed by radiation or chemotherapy. In addition, the capability of diag- nosing the type of lesion has increased by 40 percent. With molec- ular imaging, these technologies will actually be able to identify real-time cellular changes or gene expression patterns that prefigure disease. In the 30 years since they were invented, there has been a logarithmic growth in the computing power of a microchip. This growth in computing power was predicted by Gordon Moore, one of the founders of Intel, in 1967. In one of the most extraordinary (self-fulfilling) predictions in the history of technology, Moore said that the power of a microchip would double every 18 months with cost remaining constant (Figure 2. More powerful computing engines mean more rapid acquisition of images and more options for manipulating and reconstructing these images. Today, these modalities stand on the brink of eliminating the need for invasive procedures, such as colonoscopy and coronary angiography, and are capable of produc- ing remarkable three-dimensional images of functioning internal organs. Changes in Radiology Two key changes in radiology—teleradiology and machine inter- pretation of radiological images—have been made possible by the successful standardization of formats for digital radiological im- ages. With the advent of broad- band Internet connections, radiological images can not only be transmitted instantaneously inside hospitals or clinics, but they can also be sent virtually anywhere in the world where someone is avail- able to interpret them. Teleradiology has created service opportunities for isolated rural hospitals and practitioners who cannot afford full-time sub- specialized radiology coverage. Advances in image recognition software will enable radiology equipment to interpret as well as create radiological images. Recent studies have established that machine-read mammograms detect more lesions and stage them more accurately than do human radi- ologists. Human judgment will be focused on the “tough calls,” the machine-identified exceptions that require overreading. Remote Monitoring In Philadelphia recently, a newly formed technology firm, Car- dioNet, created the first regional wireless network to monitor ambu- latory cardiac patients. This device is contained in a wireless sending unit the size of a personal digital assistant, which transmits the signals to a base station where human operators are assisted by continuous computer monitoring of their heart rhythms. If the patient appears to be experiencing cardiac distress, a voice channel will enable the operator to communicate directly with the patient, verify his or her condition orally, and direct him or her to take action. The system automatically alerts the patient’s physician to the problem and can even trigger an ambulance call to bring the patient to the hospital if required. Taking this process to the next step, Medtronic, the technology leader in cardiac pacemakers, has developed an implantable device that monitors, stores, and transmits information about the patient’s cardiac rhythm directly to the patient’s physician. These devices can be programmed (and reprogrammed remotely) to vary pacing depending on the patient’s unique needs and can also administer an electric shock to restart the patient’s heart if it moves into atrial fibrillation. Progress in miniature sensing technologies is creating a new gen- eration of devices that can be worn or embedded in people’s homes to monitor their health noninvasively and automatically alert fam- ily or caregivers if problems arise. The “smart shirt,” for example, enables monitoring of multiple vital signs (heart rhythms and res- piration) and transmittal of aberrant results to family or the care team. These same technologies, when embedded in the home envi- ronment, will enable one to determine if an elderly person has fallen, is having trouble breathing, has taken prescribed medications, or is eating. In November 1999, clinicians in New York City made history by successfully performing a colecystectomy on a patient in Stras- bourg, France. These same technologies will enable students to learn via “virtual” surgical procedures us- ing interactive software that reflects to them real world images of internal organs. Telepresence technologies are producing live, three- dimensional images of internal organs, which enable physicians and their students to “tour” the body of a patient virtually. Voice response technolo- gies are likely to play an important, augmenting role in connecting patients and people at risk to the health system. During the Inter- net frenzy, many observers dismissed the voice channel of telephone communication, assuming that most of it would be displaced by digital data. The software algorithm at the heart of Eliza is so sophisticated that it can recognize and respond to millions of responses to the question, “Is this Jeff Gold- smith? Eliza is so warm and accepting that patients or family members will reliably return its calls if they are not at home to receive the initial call. However, no one would quarrel with the assertion that no technology since the invention of the telephone has created such a sensation as the Internet. The Internet enabled instantaneous and asynchronous commu- nication between any parties connected at first to an existing tele- phone network and later using cable, ground-based wireless, and satellite modalities, allowing the Internet to be accessed in auto- mobiles, airplanes, or literally anywhere in the world where one can receive a wireless signal. With increased bandwidth has come 28 Digital Medicine the ability to transmit virtually any form of digital information, including sound and both still and streaming video files. People who use the Internet are on a mission; they actively use the Internet to seek knowledge and connection to others. Society seemed to awaken in the mid-1990s to discover that it had grown a whole new nervous system. The connections spawned by the Internet have resulted in the spontaneous formation of what futurist Howard Rheingold dubbed “virtual communities” revolv- ing around common interests and issues. Six million people use the Internet to seek health information every day, just in the United States,37 and according to the Pew Trust Internet American Life Project, 62 per- cent of adults connected to the Internet sought health information through it. Internet applications have empowered consumers to define their own medical reality and to reframe and broaden their relationships to physicians. The Internet and Health Plans As discussed in Chapter 6, the Internet has also brought a host of powerful new applications to health plans to communicate with their vast and diffuse networks of subscribers, corporate customers, and the health system itself. These applications form the core of an emerging “consumer directed” model of health insurance. The Internet and Healthcare Delivery Internet applications have less direct saliency to hospitals and other healthcare delivery institutions, where improving clinical and fi- nancial operations is the most immediate management challenge. However, Internet technologies will be used to make hospitals and the information they contain more accessible to patients and fam- ilies. The Internet will enable the birth of a huge new industy of business process outsourcing in healthcare. Overall Effects of the Internet on Healthcare What the Internet has provided is affordable and nearly universal connectivity, enabling physicians and consumers to connect to one another and to the rest of the health system through their existing communications channels, such as the telephone line or cable. By democratizing connectivity, the Internet has brought the health system and its users closer together. The Internet has also provided a new communications back- bone to speed transactions and reduce clerical expenses in the vast bureaucratic sprawl that the American health system has become. As discussed earlier in this chapter, it has also provided a readily usable platform for projecting clinical information across different care sites. One way to think about the Internet is as a technology enabler or, in military jargon, a force multiplier, that helps lower 30 Digital Medicine communications and transaction cost, time, and complexity. It is also a lubricant of information flow and a solvent of organizational boundaries. It may take at least another decade before the health system realizes the full extent of its transformative potential. However, the reason why digi- tizing vital health information is important is that it enables this information to be assembled electronically and directed to the point of medical decisions. Early experimenters included academic health centers like the University of Indiana and Boston’s Brigham and Women’s Hospital and multihospital systems like Utah’s Intermountain Health Care. These early efforts involved creating a clinical data repository into which medical record in- formation was entered for later retrieval and analysis. Physicians and other caregivers could then enter orders electronically and get test results and other clinical data on their patients. However, data storage and database management technologies were so primitive, and computing power so modest, that it was extremely cumbersome for physicians to retrieve information at the point of care (e. Until the advent of the Apple Macintosh and, later, the Windows operating system in the mid-1980s, physicians who wanted to undertake computerized physician order entry had to learn an awkward language of computer commands and type those commands into the computer to manage their patients or to retrieve or use clinical information. As will be seen in Chapter 3, these efforts were also hampered by the highly fragmented record structure of hospitals. Hospitals 32 Digital Medicine have historically maintained separate record systems in each clinical department (for the laboratory, the operating room, the radiology department, the emergency room, etc. These so-called “legacy” systems were constructed primarily for billing purposes, not for care management. Legacy clinical systems are like a gigantic tangle of weedy undergrowth that strangles the care process as well as the efforts of those nurses, physicians, and other caregivers who use them. Even small hospitals may have as many as two dozen legacy clin- ical information systems. Unbelievably, large health systems with multiple hospitals may have as many as 500 legacy systems, pur- chased from different vendors, written in different software lan- guages, and operating on different, often incompatible hardware. As a consequence of this tangle, slightly different versions of our clinical reality exist in as many as 15 different places inside the hospital. The fact that there is no unified picture of an individual’s health status is a hazard to that person’s health. Creating a unified repository of all information requires a common format for clinical information, a single patient identifier applied across departments, and an agreement by all those who provide care to contribute what they know to the digital record. Clinical Decision Support Clinical decision support played an increasingly prominent role in emerging clinical systems. In the mid-1980s, intensive care special- ists at George Washington University led by Dr. Altogether, these tools may be the most complex commercial software products ever built, considering that they are automating what may be the most complex process in the economy—health service. Clinical systems are becoming “context aware,” meaning that they will be wired to diagnostic devices and patient monitoring equipment. They can track real-time changes in the patient’s health and will follow patients as they move through different levels of care—from an ambulatory diagnosis through surgery, into recov- ery, or even into home healthcare. These new systems now alert care providers when the patient’s condition changes, prompting the clinical team to take specific actions to deal with an emerging problem. Most importantly, however, clinical systems are reaching a suf- ficient level of intelligence to bring up-to-date medical knowledge to the physician’s office, exam room, or hospital bed. As medical science better defines how to treat patients, that knowledge will flow through computer systems to the point of care. The clinical system will prompt physicians, nurses, and others involved in patient care to follow the care pathway that holds the most promise for improving the patient’s health. The Clinician’s Role These systems do not relieve physicians and the care team of their professional and moral obligation in making patient care decisions.

The sample sizes for the Preg- nant and Lactating categories were very small cialis professional 20 mg with amex, so their estimates of usual intake distri- butions are not reliable purchase 40 mg cialis professional mastercard. One female was pregnant and lactating and was included in both the Pregnant and Lactating catego- ries 40 mg cialis professional overnight delivery. The sample sizes for the Pregnant and Lactating categories were very small order cialis professional 20mg with amex, so their estimates of usual intake distributions are not reliable. Infants and children fed human milk and five individuals who had no food intake for the day were excluded from the analyses. The sample sizes for the Pregnant and Lactating categories were very small, so their estimates of usual intake distributions are not reliable. Consensus Workshop on Dietary Assessment: Nutrition Monitoring and Tracking the Year 2000 Objectives. Consensus Workshop on Dietary Assessment: Nutrition Monitoring and Tracking the Year 2000 Objectives. Consensus Workshop on Dietary Assessment: Nutrition Monitoring and Tracking the Year 2000 Objectives. Consensus Workshop on Dietary Assessment: Nutrition Monitoring and Tracking the Year 2000 Objectives. Consensus Workshop on Dietary Assessment: Nutrition Monitoring and Tracking the Year 2000 Objectives. In general, brand products were not used because data for linoleic and α-linolenic acids were not available for these products. Since canola and soybean oils are the primary sources of α-linolenic acid in the U. When attempting to keep saturated fat as low as possible and linoleic and α-linolenic acid at defined levels, rich sources of monounsaturated fats were incorporated. In general, brand products were not used because data for linoleic and α-linolenic acids were not available for these products. Since canola and soybean oils are the primary sources of α-linolenic acid in the U. When attempting to keep saturated fat as low as possible and linoleic and α-linolenic acid at defined levels, rich sources of monounsaturated fats were incorporated. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactating status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactating status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. Pregnant and/or lactating women and women who had “blank but applicable” pregnancy and lactating status or who responded “I don’t know” to questions on pregnancy and lactat- ing status were excluded from all analyses. John Amatruda Daphne Pannemans Linda Bandini Renaat Philippaerts Alison Black Petra Platte L-E Bratteby Eric Poehlman Nancy Butte Andrew M. Riumallo Anne Marie Fontvieille Susan Roberts Chris Forbes-Ewan Arline Salbe Gail R. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. When ranges of intakes do not share the same letter, they are significantly different (p < 0. Individuals were assigned to ranges of energy intake from added sugars based on unadjusted Day 1 intakes. Medians, standard errors, and percents below or above the Dietary Reference Intakes were obtained using C-Side. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. Children fed human milk or who reported no food intake for a day were excluded from the analysis. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. Children fed human milk or who reported no food intake for a day were excluded from the analysis. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. Individuals were assigned to ranges of energy intake from carbohydrates based on unadjusted 2-day average intakes. Estimates of nutrient intake were adjusted using the Iowa State University method to provide estimates of usual intake. L Options for Dealing with Uncertainties Methods for dealing with uncertainties in scientific data are generally understood by working scientists and require no special discussion here except to point out that such uncertainties should be explicitly acknowl- edged and taken into account whenever a risk assessment is undertaken. More subtle and difficult problems are created by uncertainties associated with some of the inferences that must be made in the absence of directly applicable data; much confusion and inconsistency can result if they are not recognized and dealt with in advance of undertaking a risk assessment. At least partial, empirically based answers to some of these questions may be available for some of the nutrients under review, but in no case is scientific information likely to be sufficient to provide a highly certain answer; in many cases there will be no relevant data for the nutrient in question. It should be recognized that for several of these questions, certain infer- ences have been widespread for long periods of time; thus, it may seem unnecessary to raise these uncertainties anew. When several sets of animal toxicology data are available, for example, and data are not sufficient for identifying the set (i.

For multiple variables cialis professional 20mg low cost, is there some combination of risk factors that will bet- ter predict an outcome than one risk factor alone? The identification of significant risk factors can be done using multiple regressions or stepwise regression analyses as we discussed in Chapter 29 on clinical prediction rules order 40mg cialis professional free shipping. Survival analysis In the real world the ultimate outcome is often not known and could be dead as opposed to “so far cheap cialis professional 20mg mastercard, so good” or not dead yet order 40mg cialis professional otc. It would be difficult to justify waiting until all patients in a study die so that survival in two treatment or risk groups can be compared. Besides, another common problem with comparing survival between groups occurs in trying to determine what to do with patients who are doing fine but die of an incident unrelated to their medical problem such as death in a motor-vehicle accident of a patent who had a bypass graft 15 years earlier. This will alter the information used in the analysis of time to occlusion with two different types of bypasses. Finally, how should the study handle the patient who simply moves away and is lost to follow-up? The data con- sist of a time interval and a dichotomous variable indicating status, either failure (dead, graft occluded, etc. In the latter case, the patient may still be alive, have died but not from the disease of interest, or been alive when last seen but could not be located again. Early diagnosis may automatically confer longer survival if the time of diagnosis is the start time. This is also called lead-time bias, as discussed in Chapter 28, and is a common problem with screening tests. Censoring bias occurs when one of the treatment groups is more likely to be censored than the other. A survival analysis initially assumes that any patient censoring is independent of the outcome. Survival curves The distribution of survival times is most often displayed as a survivor function, also called a survival curve. It is important to note that “surviving” may indicate things other Survival analysis and studies of prognosis 365 9 x 9 x 8 O 8 O 7 x 7 x 6 6 5 x 5 x 4 4 3 O 3 O 2 x 2 x 1 x 1 x 1970 1975 1977 1980 t=0 t = 5 years Fig. Patient 1 lived longer than everyone except patient 4, although it appears that patient 1 didn’t live so long, since their previous survival (pre-1975) does not count in the analysis. We don’t know how long patient 4 will live since he or she is still alive at the end of the observation period and their data are censored at t = 5 years. Two other patients (3 and 8) are lost to follow-up, and their data are censored early (o). These curves can be deceptive since the number of individuals represented by the curve decreases as time increases. It is key that a statistical analysis is applied at several times to the results of the curves. The actuarial-life-table method measures the length of time from the moment the patient is entered into the study until failure occurs. The product-limit method is a graphic representation of the actuarial-life-table method and is also known as the Kaplan–Meier method. The analysis looks at the period of time, the month or year since the subject entered the study, in which the outcome of interest occurred. There are several tests of equality of these survivor functions or curves that are commonly performed. The Cox proportional-hazard model uses interval data as the inde- pendent variable determining how much the odds of survival are altered by each unit of change in the independent variable. This answers the question of how much the risk of stroke is increased with each increase of 10 mm Hg in mean arterial blood pressure. Further discussion of survival curves and outcome anal- ysis is beyond the scope of this book. Albert Einstein (1879–1955) Learning objectives In this chapter you will learn: r the principles of evaluating meta-analyses and systematic reviews r the concepts of heterogeneity and homogeneity r the use of L’Abbe, forest, and funnel plots´ r measures commonly used in systematic reviews: odds ratios and effect size r how to review a published meta-analysis and use the results to solve a clin- ical problem Background and rationale for performing meta-analysis Over the past 50 years there has been an explosion of research in the medi- cal literature. In the worldwide English-language medical literature alone, there were 1,300 biomedical journals in 1940, while in 2000 there were over 14,000. It has become almost impossible for the individual practitioner to keep up with the literature. This is more frustrating when contradictory studies are published about a given topic. Meta-analyses and systematic reviews are relatively new techniques used to synthesize and summarize the results of multiple research studies on the same topic. Secondary analysis is a re-analysis of the original data either using another statistical technique or answering new questions with previously obtained data. It is a summary of all pri- mary research on a given topic and it may provide good background information 367 368 Essential Evidence-Based Medicine that is more up to date than a textbook. But review articles have the disadvantage of being somewhat subjective and reflecting the biases of the author, who may be very selective of the articles chosen for review. One must be knowledgeable of the literature being reviewed in order to evaluate this type of article critically. Typically, a meta-analysis looks at data from multiple studies of the same clinical question and uses a variety of statistical techniques to integrate their findings. It may be called a quantitative systematic review and represents the rigorous application of research techniques and statistical analysis to present an overview of a given topic. It can help uncover a single study which has totally different results because of systematic error or bias in the research process. For example, multiple small trials done before 1971 showed both positive and negative effects of light or phototherapy on hyperbilirubinemia in newborns. Occasionally a large trial shows an opposite effect from that found in multiple small trials. This is often due to procedural or methodologic study design differ- ences in the trials. However, as a general rule, correctly done large cooperative trials are more reliable than meta-analysis of many smaller trials. The use of meta-analysis does not reduce the need for large well-done studies of primary clinical modalities. Guidelines for evaluation of systematic reviews Were the question and methods clearly stated and were comprehensive search methods used to locate relevant studies? In meta-analysis, the process of article selection and analysis should proceed by a preset protocol. By not changing the process in mid-analysis the author’s bias and retrospective bias are minimized. This means that the definitions of outcome and predictor or therapy variables of the analysis are not changed in Meta-analysis and systematic reviews 369 mid-stream. The research question must be clearly defined, including a defined patient population and clear and consistent definitions of the disease, interven- tions, and outcomes. A carefully defined search strategy must be used to detect and prevent publi- cation bias. This bias occurs because trials with positive results and those with large sample sizes are more likely to be published. The bibliographies of all relevant articles found should be hand searched to find any misclassified articles that were missed in the origi- nal search. The authors must cite where they looked and should be exhaustive in look- ing for unpublished studies. Not using foreign studies may introduce bias since some foreign studies are published in English-language journals while others may be missed. The authors should also contact the authors of all the studies found and ask them about other researchers working in the area who may have unpublished studies available. Also, the National Library of Medicine and the National Institutes of Health in the United States have an online repository of clinical tri- als called www. Were explicit methods used to determine which articles to include in the review and were the selection and assessment of the methodologic quality of the primary studies reproducible and free from bias? Objective selection of articles for the meta-analysis should be clearly laid out and include inclusion and exclusion criteria. This includes a clearly defined research and abstraction method and a scoring system for assessing the quality of the included studies. The publication status may sug- gest stronger studies in that those that were never published or only published in abstract form may be significantly deficient in methodological areas. A well-designed obser- vational study with appropriate safeguards to prevent or minimize bias and con- founding, will also give very strong results. The methods of meta-analysis include ranking or grading the quality of the evidence. The study sites and patient populations of the individual studies may limit generalizability of the meta-analysis. We will discuss issues of how to judge homogeneity and combine heterogeneous studies. Independent review of the methods section looks at inclusion and exclusion criteria, coding, and replication issues. There must be accurate and objective abstraction of the data, ideally done by blinded abstracters. Two abstracters should gather the data independently and the author should check for inter- rater agreement. The methods and results sections should be disguised to pre- vent reviewers from discovering the source of the research. Once this has been established, a single coder can code all the remaining study results. Were the differences in individual study results adequately explained and were the results of the primary studies combined appropriately? Testing for heterogeneity of the stud- ies is done to determine if the studies are qualitatively similar enough to com- bine. The tests for heterogeneity include the Mantel–Haentszel chi-squared test, the Breslow–Day test, and the Q statistic by the DerSimonian and Laird method. However, the absence of statistical significance does not mean homogeneity and may only be present due to low power of the statistical test for heterogeneity. The presence of heterogeneity among the studies analyzed will result in erro- neous interpretation of the statistical results. If the studies are very heteroge- neous, one strategy for analyzing them is to remove the study with most extreme or outlier results and recalculate the statistic. If the statistic is no longer statisti- cally significant, it can be assumed that the outlier study was responsible for all or most of the heterogeneity. That study should then be examined more closely to determine what about the study design might have caused the observed extreme result. This could be due to differences in the population studied or systematic bias in the conduct of the study. Analysis and aggregation of the data can be done in several ways, but should consider sample sizes and magnitude of effects. A simple vote count in which the number of studies with positive results is directly compared with the number of studies with negative results is not an acceptable method since neither effect Meta-analysis and systematic reviews 371 size nor sample size are considered.

Athletes With minor exceptions purchase 20mg cialis professional free shipping, dietary recommendations for athletes are not distinguished from the general population cheap cialis professional 20mg on-line. As described in Chapter 12 discount 20 mg cialis professional otc, the amount of dietary energy from the recommended nutrient mix should be adjusted to achieve or maintain optimal body weight for competitive athletes and others engaged in similarly demanding physical activities purchase 20 mg cialis professional mastercard. As described by Dewey and colleagues (1996), the lower value is similar to average energy expenditure of preschool children and to energy expenditure for maintenance and activity of recovering malnourished children in Peru. The higher value is typical of normal infants at 9–12 months of age, but may be higher than would be expected of malnourished children if they are less active. While some athletes may be able to sustain extremely high power outputs over days or even weeks (such as in the Tour de France bicycle race), such endeavors are episodic and cannot be sustained indefi- nitely. Despite the difference in scope of energy flux associated with partici- pation in sports and extremely demanding physical activities such as mara- thon running and military operations, several advantages are associated with different forms of exercise. For example, resistance exercise promotes muscle hypertrophy and changes in body composition by increasing the ratio of muscle to total body mass (Brooks et al. Athletes need- ing to increase strength will necessarily employ resistance exercises while ensuring that dietary energy is sufficient to increase muscle mass. Total body mass may increase, remain the same, or decrease depending on energy balance. Athletes needing to decrease body mass to obtain bio- mechanical advantages will necessarily increase total exercise energy out- put, reduce energy input, or use a combination of the two approaches. As distinct from weight loss by diet alone, having a major exercise component will serve to preserve lean body mass even in the face of negative energy balance. The ability of healthy indi- viduals to compensate for increases in energy intake by increasing energy expenditure (either for physical activity or resting metabolism) depends on physiological and behavioral factors. When individuals are given a diet providing a fixed (but limited) amount of energy in excess of the require- ments to maintain body weight, they will initially gain weight. However, over a period of several weeks, their energy expenditure will increase, mostly (Durnin, 1990; Ravussin et al. Some reports indicate that the magnitude of the reduction in energy expenditure when energy intake is reduced is greater than the corresponding increase in energy expenditure when energy intake is increased (Saltzman and Roberts, 1995). It is likely that for most individuals the principal mechanism for maintaining body weight is by controlling food intake rather than physical activity (Jequier and Tappy, 1999). This level would also provide some margin for weight gain in mid-life without surpassing the 25 kg/m2 threshold. In the case of obese individuals who need to lose weight to improve their health, energy intakes that cause adverse risk are those that are higher than those needed to lose weight without causing negative health consequences. Summary Because of the direct impact of deviations from energy balance on body weight and of changes in body weight, body-weight data represent critical indicators of the adequacy of energy intake. The uncertainty factor would be one as there is no uncertainty in the fact that overconsumption of energy leads to weight gain. Men 19 through 30 years of age had the highest reported energy intake with the 99th percentile of intake at 5,378 kcal/d. This is particularly true for young children 3 to 5 years of age, adolescent boys, and adult men and women 40 through 60 years of age. Multivariate-adjusted relative risk/hazard risk/odds ratio estimates were used in this table whenever possible. Multivariate-adjusted relative risk/ hazard risk/odds ratio estimates were used in this table whenever possible. Multivariate-adjusted relative risk/hazard risk/odds ratio estimates were used in this table whenever possible. Multivariate-adjusted relative risk/hazard risk/odds ratio estimates were used in this table whenever possible. Multivariate-adjusted relative risk/hazard risk/odds ratio estimates were used in this table whenever possible. 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