How to recognize a trustworthy clinical practice guideline
Bibliographic record
Abstract
Trustworthy clinical practice guidelines represent a fundamental tool to summarize relevant evidence regarding a set of clinical choices and provide guidance for making optimal clinical decisions. Clinicians must differentiate between guidelines that provide trustworthy evidence guidance and those that do not. We present six questions clinicians should ask when evaluating a guideline's trustworthiness. (1) Are the recommendations clear?; (2) Have the panelists considered all alternatives?; (3) Have the panelists considered all patient-important outcomes?; (4) Is the recommendation based on an up-to-date systematic review?; (5) Is the strength of the recommendation compatible with the certainty of the evidence?; (6) Might conflicts of interest influence the recommendations? If yes, were they managed? Once the conclude they are dealing with a trustworthy guideline, clinicians must gain an understanding of the transparent evidence summary that the guideline will offer, and judge the applicability of trustworthy recommendations to their patients and settings. Consideration of the circumstances and values and preferences of patients will be crucial for all weak or conditional recommendations.
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 | 0.289 | 0.745 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.031 | 0.039 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".