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Deborah J. Cook

Bio: Deborah J. Cook is an academic researcher from McMaster University. The author has contributed to research in topics: Intensive care & Intensive care unit. The author has an hindex of 173, co-authored 907 publications receiving 148928 citations. Previous affiliations of Deborah J. Cook include McMaster University Medical Centre & Queen's University.


Papers
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Journal ArticleDOI
19 Jul 2000-JAMA
TL;DR: Quantitative research is designed to test well-specified hypotheses, determine whether an intervention did more harm than good, and find out how much a risk factor predisposes persons to disease.
Abstract: Quantitative research is designed to test well-specified hypotheses, determine whether an intervention did more harm than good, and find out how much a risk factor predisposes persons to disease. Equally important, qualitative research offers insight into emotional and experiential phenomena in health care to determine what, how, and why. There are 4 essential aspects of qualitative analysis. First, the participant selection must be well reasoned and their inclusion must be relevant to the research question. Second, the data collection methods must be appropriate for the research objectives and setting. Third, the data collection process, which includes field observation, interviews, and document analysis, must be comprehensive enough to support rich and robust descriptions of the observed events. Fourth, the data must be appropriately analyzed and the findings adequately corroborated by using multiple sources of information, more than 1 investigator to collect and analyze the raw data, member checking to establish whether the participants' viewpoints were adequately interpreted, or by comparison with existing social science theories. Qualitative studies offer an alternative when insight into the research is not well established or when conventional theories seem inadequate. JAMA. 2000;284:357-362

888 citations

Journal ArticleDOI
TL;DR: A prospective multicenter cohort study in which potential risk factors for stress ulceration in patients admitted to intensive care units and the occurrence of clinically important gastrointestinal bleeding were evaluated.
Abstract: Background The efficacy of prophylaxis against stress ulcers in preventing gastrointestinal bleeding in critically ill patients has led to its widespread use. The side effects and cost of prophylaxis, however, necessitate targeting preventive therapy to those patients most likely to benefit. Methods We conducted a prospective multicenter cohort study in which we evaluated potential risk factors for stress ulceration in patients admitted to intensive care units and documented the occurrence of clinically important gastrointestinal bleeding (defined as overt bleeding in association with hemodynamic compromise or the need for blood transfusion). Results Of 2252 patients, 33 (1.5 percent; 95 percent confidence interval, 1.0 to 2.1 percent) had clinically important bleeding. Two strong independent risk factors for bleeding were identified: respiratory failure (odds ratio, 15.6) and coagulopathy (odds ratio, 4.3). Of 847 patients who had one or both of these risk factors, 31 (3.7 percent; 95 percent confidence ...

882 citations

Journal ArticleDOI
13 Dec 1995-JAMA
TL;DR: An approach to classifying strength of recommendations is suggested and is directed primarily at clinicians who make treatment recommendations that they hope their colleagues will follow.
Abstract: THE ULTIMATE PURPOSE of applied health research is to improve health care. Summarizing the literature to adduce recommendations for clinical practice is an important part of the process. Recently, the health sciences community has reduced the bias and imprecision of traditional literature summaries and their associated recommendations through the development of rigorous criteria for both literature overviews 1-3 and practice guidelines. 4,5 Even when recommendations come from such rigorous approaches, however, it is important to differentiate between those based on weak vs strong evidence. Recommendations based on inadequate evidence often require reversal when sufficient data become available, 6 while timely implementation of recommendations based on strong evidence can save lives. 6 In this article, we suggest an approach to classifying strength of recommendations. We direct our discussion primarily at clinicians who make treatment recommendations that they hope their colleagues will follow. However, we believe that any clinician who attends to

829 citations

Journal ArticleDOI
TL;DR: It is concluded that VAP prolongs ICU length of stay and may increase the risk of death in critically ill patients and the attributable risk of VAP appears to vary with patient population and infecting organism.
Abstract: To evaluate the attributable morbidity and mortality of ventilator-associated pneumonia (VAP) in intensive care unit (ICU) patients, we conducted a prospective, matched cohort study. Patients expected to be ventilated for > 48 h were prospectively followed for the development of VAP. To determine the excess ICU stay and mortality attributable to VAP, we matched patients with VAP to patients who did not develop clinically suspected pneumonia. We also conducted sensitivity analyses to examine the effect of different populations, onset of pneumonia, diagnostic criteria, causative organisms, and adequacy of empiric treatment on the outcome of VAP. One hundred and seventy-seven patients developed VAP. As compared with matched patients who did not develop VAP, patients with VAP stayed in the ICU for 4.3 d (95% confidence interval [CI]: 1.5 to 7.0 d) longer and had a trend toward an increase in risk of death (absolute risk increase: 5.8%; 95% CI: − 2.4 to 14.0 d; relative risk (RR) increase: 32.3%; 95% CI: − 20....

826 citations


Cited by
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Journal ArticleDOI
TL;DR: Moher et al. as mentioned in this paper introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses, which is used in this paper.
Abstract: David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses

62,157 citations

Journal Article
TL;DR: The QUOROM Statement (QUality Of Reporting Of Meta-analyses) as mentioned in this paper was developed to address the suboptimal reporting of systematic reviews and meta-analysis of randomized controlled trials.
Abstract: Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews. Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7 In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1). Box 1 Conceptual issues in the evolution from QUOROM to PRISMA

46,935 citations

Journal ArticleDOI
04 Sep 2003-BMJ
TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

45,105 citations

Journal ArticleDOI
TL;DR: A structured summary is provided including, as applicable, background, objectives, data sources, study eligibility criteria, participants, interventions, study appraisal and synthesis methods, results, limitations, conclusions and implications of key findings.

31,379 citations