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 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 | 0.305 | 0.719 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".