Guideline Assessment Project: Filling the GAP in Surgical Guidelines
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to identify clinical practice guidelines published by surgical scientific organizations, assess their quality, and investigate the association between defined factors and quality. The ultimate objective was to develop a framework to improve the quality of surgical guidelines. SUMMARY BACKGROUND DATA: Evidence on the quality of surgical guidelines is lacking. METHODS: We searched MEDLINE for clinical practice guidelines published by surgical scientific organizations with an international scope between 2008 and 2017. We investigated the association between the following factors and guideline quality, as assessed using the AGREE II instrument: number of guidelines published within the study period by a scientific organization, the presence of a guidelines committee, applying the GRADE methodology, consensus project design, and the presence of intersociety collaboration. RESULTS: Ten surgical scientific organizations developed 67 guidelines over the study period. The median overall score using AGREE II tool was 4 out of a maximum of 7, whereas 27 (40%) guidelines were not considered suitable for use. Guidelines produced by a scientific organization with an output of ≥9 guidelines over the study period [odds ratio (OR) 3.79, 95% confidence interval (CI), 1.01-12.66, P = 0.048], the presence of a guidelines committee (OR 4.15, 95% CI, 1.47-11.77, P = 0.007), and applying the GRADE methodology (OR 8.17, 95% CI, 2.54-26.29, P < 0.0001) were associated with higher odds of being recommended for use. CONCLUSIONS: Development by a guidelines committee, routine guideline output, and adhering to the GRADE methodology were found to be associated with higher guideline quality in the field of surgery.
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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 | 0.189 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".