Systematic Reviews To Support Evidence-Based Medicine: How To Review and Apply Findings of Healthcare Research
Bibliographic record
Abstract
Resource CornerJanuary 1, 2004Systematic Reviews To Support Evidence-Based Medicine: How To Review and Apply Findings of Healthcare ResearchSharon E. Straus, MD, MSc, FRCPC, Darlyne Rath, MScSharon E. Straus, MD, MSc, FRCPCUniversity of Toronto, Toronto, Ontario, Canada (S.E.S., D.R.)University of Toronto, Toronto, Ontario, Canada (S.E.S., D.R.)Search for more papers by this author, Darlyne Rath, MScUniversity of Toronto, Toronto, Ontario, Canada (S.E.S., D.R.)University of Toronto, Toronto, Ontario, Canada (S.E.S., D.R.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2004-140-1-A15 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail This compact book attempts to guide the reader through the process of appraising and conducting a systematic review of the literature. It is divided into 3 sections: an introduction, the steps of a systematic review, and case studies. The introduction describes the goals of the book and how to use it. It provides a list of sources of systematic reviews and guidelines with relevant Web addresses. The next section is divided into 5 chapters that address framing a question, identifying the relevant literature, assessing the quality of literature, summarizing the evidence, and interpreting the findings. Each chapter provides an explanation ... Author, Article, and Disclosure InformationAffiliations: University of Toronto, Toronto, Ontario, Canada (S.E.S., D.R.)University of Toronto, Toronto, Ontario, Canada (S.E.S., D.R.) Previousarticle Advertisement FiguresReferencesRelatedDetails January 1, 2004Volume 140, Issue 1Page: A15KeywordsEvidence based medicineHealth services researchPrevention, policy, and public healthSafetySystematic reviews ePublished: 9 March 2020 Issue Published: January 1, 2004 Copyright & PermissionsCopyright © 2004 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.115 | 0.383 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.041 | 0.037 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".