Development of a standardized, comprehensive "ideal drug detail".
Bibliographic record
Abstract
OBJECTIVES: To develop a standardized, comprehensive ideal drug detail for use in face-to-face education about individual drugs. METHODS: A random sample of 603 physicians and pharmacists was selected and stratified to include input from each of the following specialties: family practice, internal medicine, surgery, pediatrics, psychiatry, obstetrics/gynecology, geriatric medicine and clinical pharmacology. Thirty-one potential items were generated by the investigators from a preliminary survey of a local convenience sample of physicians and pharmacists. A modified Delphi consensus process was used in the large sample to determine which items should be included in the ideal drug detail. In each round of the Delphi process, respondents rated each item on a seven-point scale of importance and were then given feedback of the cumulative ratings for each item. Rounds were continued until consensus was obtained on all items. RESULTS: The response rate to the first round was 55.3%; 85.5% of these respondents responded to the second round. Response rates varied between specialties from 44% to 70%. Attempts to contact nonresponders to measure potential nonrespondent bias were unsuccessful. Consensus was obtained on 19 items after the first round, and on the remaining 12 items after the second round. Four items were dropped because they were unimportant. There was variation in modal response between specialties on eight items. CONCLUSIONS: Consensus was obtained among a sizable and interested sample of Canadian physicians and pharmacists on the items of information needed to prescribe a drug appropriately. Subsequent work will refine this list into a usable template to develop ideal drug details for specific drugs, to develop an assessment process to measure quality of information, and to assess the impact of this program on prescribing and patient outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".