A ‘reluctant’ critical review: ‘Manual for evidence‐based clinical practice (2015)’
Bibliographic record
Abstract
BACKGROUND: The Users' Guides to the Medical Literature Manual has been a major influence on the teaching and practice of health care globally. METHODS: The 3rd edition of the multi-authored Manual was reviewed using the principles outlined in Evidence-based Medicine (EBM) texts. One 'clinical scenario' was selected for critical appraisal, as were several chapters; objectivity was enhanced by citing references to support opinions. RESULTS (SUMMARY OF THE APPRAISAL): (1) Strengths: Clinical pearls, too numerous to list. EXAMPLES: (i) evidence is never enough to drive clinical decision making; (ii) do not rush to adopt new interventions; and (iii) question efficacy data based only on surrogate markers. (2) Weaknesses: The Manual shares shortcomings of textbooks discussed by Straus et al.: (i) references may not be current, important ones may be excluded and citations may be selective; (ii) often, opinion-based; and (iii) delays between revisions. (3) Notable omissions: Little or no discussion of: (i) important segments of the population: those <18 years of age, >65 years of age and those with multimorbidity; (ii) surgical disciplines; (iii) Greenhalgh et al.'s essay on EBM; (iv) alternate views on the hierarchy of evidence; and (vi) critical thinking. (4) Additional issues: (i) Omission of important references on dabigatran (clinical scenario: chapter 13.1); (ii) authors' advice (Chapter 13.3) to 'bypass the discussion section of published research'; and (iii) the advocacy of pre-appraised sources of evidence and network meta-analysis without warnings about limitations, are critiqued. CONCLUSION: The Manual has several clinical pearls but readers should also be aware of shortcomings.
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How this classification was reachedexpand
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 | 0.292 | 0.614 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.042 | 0.041 |
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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".