Impact of Clinician Judgement on Formulary Committees’ Recommendations in Canada
Bibliographic record
Abstract
OBJECTIVES: In formulary committee deliberations, evidence for the efficacy of medications is often based on changes in the scale scores of patient-reported outcome measures. Our aim was to examine whether clinician judgement about the efficacy of medications for Alzheimer's disease, when added to scale score evidence, affects formulary committee members' recommendations about providing these medications under public insurance. METHODS: The study was conducted using mixed methods. In a survey of formulary committee members in Canada, 32 participants were presented with scenarios that outlined different levels of efficacy for a medication. For each scenario, participants were asked to specify their likelihood of recommending that the medication be provided under public insurance. Of the 32 participants, 23 agreed to take part in an interview to explain the survey results. Content analysis was used to elicit recurrent themes across the interviews. RESULTS: When a medication was disease modifying, use of clinician judgement increased the mean likelihood of recommending that the medication be provided under public insurance. Despite this, some participants felt formulary committees should not use clinician judgement because of risks of subjectivity and bias. However, other participants believed the addition of clinician judgement would enhance the clinical relevance of evidence that might otherwise be based entirely on changes in scale score. CONCLUSIONS: Clinician judgement about the efficacy of medications can influence formulary committee recommendations. This suggests the need for a new approach to govern the consideration of expert evidence during formulary committee deliberations.
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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 | 0.351 | 0.677 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".