028 How Do Clinicians Like and Understand Trustworthy Guidelines? Randomised Controlled Trial Using Clickers in Educational Sessions
Bibliographic record
Abstract
Background Clinical practice guidelines (CPG) often have shortcomings in presentation formats that limit dissemination at the point of care. As part of the DECIDE project we have developed multilayered CPG presentation formats. Comprehensive user-testing of the formats has provided us with alternative presentation formats now ready for randomised trials but also an important insight: Insufficient conceptual understanding of guideline methodology (e.g. strength of recommendations and quality of evidence) may hamper application of CPG recommendations in practice. Objectives To determine physicians’ understanding, attitudes and preferences concerning trustworthy guidelines in traditional and new presentation formats (DECIDE A and B). Methods In this randomised controlled trial we will recruit 100 physicians attending a standardised lecture with 3 components: 1) presentation of clinical scenario, 2) explanations of key concepts of trustworthy CPG (e.g. GRADE, AGREE II) and 3) presentation of a current trustworthy CPG relevant to the scenario, displayed in traditional PDF format and DECIDE A and B formats. Throughout the lecture participants will answer questions with ‘Clickers’ and be randomly assigned to alternative presentation formats by concealed allocation and blinding, through the use of eyepatches. Results We will present results from the trial at the conference. Discussion If our approach of integrating randomised trials into educational sessions is feasible and provides valid results we will conduct multiple such trials in DECIDE. Implications for Guideline Developers and Users Optimised GL presentation formats and sufficient conceptual understanding, as researched in this trial, should facilitate the uptake of trustworthy CPG and application of research evidence in practice.
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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 | 0.072 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".