Approaches of integrating the development of guidelines and quality indicators: a systematic review
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
BACKGROUND: Guidelines and quality indicators (for example as part of a quality assurance scheme) aim to improve health care delivery and health outcomes. Ideally, the development of quality indicators should be grounded in evidence-based, trustworthy guideline recommendations. However, anecdotally, guidelines and quality assurance schemes are developed independently, by different groups of experts who employ different methodologies. We conducted an extension and update of a previous systematic review to identify, describe and evaluate approaches to the integrated development of guidelines and related quality indicators. METHODS: On May 24th, 2019 we searched in Medline, Embase and CINAHL and included studies if they reported a methodological approach to guideline-based quality indicator development and were published in English, French, or German. RESULTS: Out of 16,034 identified records, we included 17 articles that described a method to integrate guideline recommendations development and quality indicator development. Added to the 13 method articles from original systematic review we included a total 30 method articles. We did not find any evaluation studies. In most approaches, guidelines were a source of evidence to inform the quality indicator development. The criteria to select recommendations (e.g. level of evidence or strength of the recommendation) and to generate, select and assess quality indicators varied widely. We found methodological approaches that linked guidelines and quality indicator development explicitly, however none of the articles reported a conceptual framework that fully integrated quality indicator development into the guideline process or where quality indicator development was part of the question formulation for developing the guideline recommendations. CONCLUSIONS: In our systematic review we found approaches which explicitly linked guidelines with quality indicator development, nevertheless none of the articles reported a comprehensive and well-defined conceptual framework which integrated quality indicator development fully into the guideline development process.
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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 | 0.044 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it