International Assessment of the Quality of Clinical Practice Guidelines in Oncology Using the Appraisal of Guidelines and Research and Evaluation Instrument
Bibliographic record
Abstract
PURPOSE: To describe the quality of oncology guidelines developed in different countries. METHODS: The Appraisal of Guidelines and Research and Evaluation (AGREE) Instrument was used to assess the quality of 100 guidelines (including 32 oncology guidelines) from 13 countries. The criteria of the instrument are grouped into six quality domains: scope and purpose, stakeholder involvement, rigor of development, clarity and presentation, applicability, and editorial independence. RESULTS: Oncology guidelines had significantly higher scores on rigor of development than nononcology guidelines (42.2% v 29.4%; P =.02). In particular, systematic methods to search for evidence were more often used (P =.01); the methods for formulating the recommendations were more clearly described (P =.02); and health benefits, risks, and side effects were more often considered in formulating the recommendations (P =.03). Although the standardized scores for the other domains were not significantly different, the oncology guidelines had significantly higher scores for items measuring inclusion of all relevant professional groups (P =.05), consideration of patient views (P =.04), and presentation of different options (P =.05). Only three organizations producing oncology guidelines had standardized scores more than 60% for more than three domains. CONCLUSION: The quality of clinical practice guidelines (CPGs) is modest in general, but for certain domains, oncology guidelines seem to be of better quality than others. The experience of the organization may explain higher scores for some items. Research projects and training aimed at improving the quality of guidelines should be developed. The AGREE instrument could provide a basis for defining steps in a shared development approach to produce high-quality CPGs.
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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 | 0.298 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".