How Evidence-Based Are the Recommendations in Evidence-Based Guidelines?
Bibliographic record
Abstract
BACKGROUND: Treatment recommendations for the same condition from different guideline bodies often disagree, even when the same randomized controlled trial (RCT) evidence is cited. Guideline appraisal tools focus on methodology and quality of reporting, but not on the nature of the supporting evidence. This study was done to evaluate the quality of the evidence (based on consideration of its internal validity, clinical relevance, and applicability) underlying therapy recommendations in evidence-based clinical practice guidelines. METHODS AND FINDINGS: A cross-sectional analysis of cardiovascular risk management recommendations was performed for three different conditions (diabetes mellitus, dyslipidemia, and hypertension) from three pan-national guideline panels (from the United States, Canada, and Europe). Of the 338 treatment recommendations in these nine guidelines, 231 (68%) cited RCT evidence but only 105 (45%) of these RCT-based recommendations were based on high-quality evidence. RCT-based evidence was downgraded most often because of reservations about the applicability of the RCT to the populations specified in the guideline recommendation (64/126 cases, 51%) or because the RCT reported surrogate outcomes (59/126 cases, 47%). CONCLUSIONS: The results of internally valid RCTs may not be applicable to the populations, interventions, or outcomes specified in a guideline recommendation and therefore should not always be assumed to provide high-quality evidence for therapy recommendations.
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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 | 0.504 | 0.897 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".