Use of systematic reviews in clinical practice guidelines: case study of smoking cessation
Bibliographic record
Abstract
OBJECTIVE: To examine the extent to which recommendations in the national guidelines for the cessation of smoking are based on evidence from systematic reviews of controlled trials. DESIGN: Retrospective analysis of recommendations for the national guidelines for the cessation of smoking. MATERIALS: National guidelines in clinical practice on smoking cessation published in English. MAIN OUTCOME MEASURES: The type of evidence (systematic review of controlled trials, individual trials, other studies, expert opinion) used to support each recommendation. We also assessed whether a Cochrane systematic review was available and could have been used in formulating the recommendation. RESULTS: Four national smoking cessation guidelines (from Canada, New Zealand, the United Kingdom, and the United States) covering 105 recommendations were identified. An explicit evidence base for 100%, 89%, 68%, and 98% of recommendations, respectively, was detected, of which 60%, 56%, 59%, and 47% were based on systematic reviews of controlled studies. Cochrane systematic reviews could have been used to develop between 39% and 73% of recommendations but were actually used in 0% to 36% of recommendations. The UK guidelines had the highest proportion of recommendations based on Cochrane systematic reviews. CONCLUSIONS: Use of systematic reviews in guidelines is a measure of the "payback" on investment in research synthesis. Systematic reviews commonly underpinned recommendations in guidelines on smoking cessation. The extent to which they were used varied by country and there was evidence of duplication of effort in some areas. Greater international collaboration in developing and maintaining an evidence base of systematic reviews can improve the efficiency of use of research resources.
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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 | 0.391 | 0.773 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.040 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".