Developing a Clinician Friendly Tool to Identify Useful Clinical Practice Guidelines: G-TRUST
Bibliographic record
Abstract
BACKGROUND: Clinicians are faced with a plethora of guidelines. To rate guidelines, they can select from a number of evaluation tools, most of which are long and difficult to apply. The goal of this project was to develop a simple, easy-to-use checklist for clinicians to use to identify trustworthy, relevant, and useful practice guidelines, the Guideline Trustworthiness, Relevance, and Utility Scoring Tool (G-TRUST). METHODS: A modified Delphi process was used to obtain consensus of experts and guideline developers regarding a checklist of items and their relative impact on guideline quality. We conducted 4 rounds of sampling to refine wording, add and subtract items, and develop a scoring system. Multiple attribute utility analysis was used to develop a weighted utility score for each item to determine scoring. RESULTS: Twenty-two experts in evidence-based medicine, 17 developers of high-quality guidelines, and 1 consumer representative participated. In rounds 1 and 2, items were rewritten or dropped, and 2 items were added. In round 3, weighted scores were calculated from rankings and relative weights assigned by the expert panel. In the last round, more than 75% of experts indicated 3 of the 8 checklist items to be major indicators of guideline usefulness and, using the AGREE tool as a reference standard, a scoring system was developed to identify guidelines as useful, may not be useful, and not useful. CONCLUSION: The 8-item G-TRUST is potentially helpful as a tool for clinicians to identify useful guidelines. Further research will focus on its reliability when used by clinicians.
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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 | 0.106 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".