30 Using an adapted version of the G-TRUST to reduce overuse and overdiagnosis through appropriate guideline selection
Bibliographic record
Abstract
Clinicians are regularly faced with multiple and often conflicting guidelines. While clinicians may wish to reduce overuse in their practice, they may find doing so challenging when some guidelines regularly advocate or encourage low-value practices. The G-TRUST tool can be used to compare clinical practice guidelines and select the most useful or appropriate one in a given context. While this tool has been validated in the past, its length may limit its use in daily practice. We suggest an adapted and shortened version of the G-TRUST tool with only 3 questions to facilitate its use. Participants will be able to apply the tool in three different contexts presented by the facilitators (screening for dyslipidemia, screening for osteoporosis, and screening for diabetes) to appreciate how this tool can help select useful guidelines to reduce overuse and overdiagnosis. They will be invited to apply the tool to a domain or guideline of their choice and appreciate how it may facilitate discussion with colleagues and potentially reduce overuse and overdiagnosis. Objectives Appreciate how variation in different clinical practice guidelines may be a driver for overuse and Overdiagnosis. Familiarize themselves with the G-TRUST tool, as well as the author’s simplified version of the tool. Apply the simplified G-TRUST tool in different screening contexts suggested by the workshop facilitators to reduce overuse and Overdiagnosis. Apply the G-TRUST tool to compare two clinical practice guidelines on a topic of their choice to appreciate how it may be used to foster implementation of guidelines that reduce overuse and overdiagnosis at the consultation level. Method Practical workshop. Case studies in small groups where participants will be asked to compare clinical practice guidelines. Small group discussions on how the tool may be helpful to select guidelines that reduce overuse and overdiagnosis. Results In our experience, the modified G-TRUST allows selection of more appropriate clinical practice guidelines, that minimize overuse and overdiagnosis. This tool focuses on three major aspects of the G Trust and by simplifying the tool it allows to be possibly usable in clinical practice. Conclusions The adapted version of the G-TRUST tool can help select useful guidelines to reduce overuse and overdiagnosis at the consultation level.
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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 | 0.033 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".