Do Clinical Practice Guidelines Reflect Research Evidence?
Bibliographic record
Abstract
OBJECTIVES: To examine whether existing clinical practice guidelines (CPGs) for cholesterol testing reflect research evidence and hence may control or reduce costs while maintaining or improving the quality of care. METHODS: A systematic search for published and unpublished cholesterol testing CPGs and independent critical appraisal of the CPGs by two researchers using a standard checklist. RESULTS: In four of the five CPGs analysed, the link between the research evidence and the recommendations was not maintained. The appraisal, local experience and the literature all suggest that panel composition is an important explanation, in that the greater the involvement of clinical experts in the development process of the CPGs, the less the recommendations reflected the research evidence. Even though their participation is important for CPG uptake, clinical expert panels appear to have difficulty limiting CPGs to research-based recommendations. CONCLUSIONS: Existing cholesterol testing CPGs are unlikely to improve the quality of care while controlling or reducing costs. The problem lies not with guideline implementation but with the guidelines themselves. It is unclear how best to ensure that recommendations reflect research evidence but this is likely to require significant and progressive changes to the current guideline development process, including a redefinition of the clinical experts' role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.433 | 0.873 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".