Exploring the Use of a Participative Design in the Early Development of a Predictive Test: The Importance of Physician Involvement
Bibliographic record
Abstract
In this study, we contribute to the personalized medicine and health care management literature by developing and testing a new participative design approach. We propose that involving gastroenterologists in the development of a predictive test to assist them in their clinical decision-making process for the treatment of inflammatory bowel diseases will increase the likelihood of their acceptance of the innovation. Based on data obtained from 6 focus groups across Canada from a total of 28 physicians, analyses reveal that current tools do not enable discriminating between treatment options to find the best fit for each patient. Physicians expect a new predictive tool to have the capability of showing clear reliability and significant benefits for the patient, while being accessible in a timely manner that facilitates clinical decisions. Physicians also insist on their key role in the implementation process, hence confirming the relevance and importance of participative designs in personalized medicine.
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How this classification was reachedexpand
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 | 0.032 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".