The GIN-McMaster guideline tool extension for the integration of quality improvement and quality assurance in guidelines: a description of the methods for its development
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Our objective was to develop an extension of the widely used GIN-McMaster Guideline Development Checklist and Tool for the integration of quality assurance and improvement (QAI) schemes with guideline development. METHODS: We used a mixed-methods approach incorporating evidence from a systematic review, an expert workshop and a survey of experts to iteratively create an extension of the checklist for QAI through three rounds of feedback. As a part of this process, we also refined criteria of a good guideline-based quality indicator. RESULTS: We developed a 40-item checklist extension addressing steps for the integration of QAI into guideline development across the existing 18 topics and created one new topic specific to QAI. The steps span from 'organization, budget, planning and training', to updating of QAI and guideline implementation. CONCLUSION: The tool supports integration of QAI schemes with guideline development initiatives and it will be used in the forthcoming integrated European Commission Initiative on Colorectal Cancer. Future work should evaluate this extension and QAI items requiring additional support for guideline developers and links to QAI schemes.
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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 | 0.147 | 0.262 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".