P352 Game-It (Games For Improving Treatment-Recommendations)
Bibliographic record
Abstract
Background Traditionally patients are not involved in the development of clinical guidelines, and most current panels include only clinical and methodological experts. We therefore know little about what patients (or healthy lay people) would have recommended if they were provided with the same evidence as experts. Objectives Develop a prototype ‘Recommendation-making game’ which can be used for: (1) Exploring patients and lay people’s reasoning when facing the same evidence as an expert guideline panel; (2) assess whether they give similar value to the outcomes or burdens if the decision of making a recommendation for a patient group was up to them; (3) determine whether their recommendation concur with what they would have decided for themselves. Methods We used game technology to make a generic prototype of an online “Recommendation-making game”, based on structured guidelines published in the MAGIC (Making Grade the Irresistible Choice) application. This approach will enable us to automatically make online surveys out of any guideline/recommendation in the system. In making it into a game we believe people would want to participate, and we can potentially harvest information from a large group of people. The game can also be used in small focus groups for qualitative data collection. Results We will display the prototype at the conference. Discussion Does clinical experts reasoning effects that of patient representatives in a guideline panel? Implications for Guideline Developers/Users GAME-IT explores a new way of harvesting information from patients (or healthy lay people) regarding treatment recommendations.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 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".