Characteristics of high-quality guidelines: evaluation of 86 clinical guidelines developed in ten European countries and Canada.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To identify predictors of high-quality clinical practice guidelines. METHODS: A total of 86 guidelines from 11 countries were assessed by four independent appraisers per guideline using the AGREE instrument (23 items). Six aspects of guideline development were considered to explain the variation in quality scores: care level (primary/secondary care), scope (diagnosis/treatment), type of guideline (new/update), year of publication, type of agency (governmental/professional), and whether the guideline was produced within a structured and coordinated program. RESULTS: Guidelines produced within a guideline program and by governmental agencies had higher scores than their counterparts. Differences in the applicability of the guidelines could not be explained by the variables studied. CONCLUSION: To ensure high quality, clinical guidelines should be produced within a structured and coordinated program. Professional organizations or specialist societies that aim to develop guidelines may adopt quality criteria from leading guideline agencies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it