Developing a Clinician Friendly Tool to Identify Useful Clinical Practice Guidelines: G-TRUST
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: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.363 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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