How Many “Me-Too” Drugs are Enough? The Case of Physician Preferences for Specific Statins
Bibliographic record
Abstract
BACKGROUND: The increasing availability of "Me-Too" drugs has provided considerable treatment options for clinicians. However, the number of such drugs within a class that are actually used by clinicians has not been well studied. OBJECTIVE: To determine the number of different statins that individual physicians use in practice. METHODS: The Ontario Drug Benefit database was used to identify physicians who issued at least 10 incident statin prescriptions between October 2001 and May 2003 for patients aged 66 years and older. A preferred statin was defined for each physician, and the proportion of each physician's incident prescriptions written for that agent was determined. We then determined the number of different statins required to fill each physician's incident prescribing needs. RESULTS: A total of 3426 physicians wrote 73,571 incident statin prescriptions. The mean percentage of prescriptions written for each physician's preferred statin formulation was 73.7%. Repeat analysis to examine the proportion of prescriptions filled using each physician's top 2 statin formulations found that the average physician wrote the vast majority of his or her incident prescriptions (94.9%) for only 1 or 2 statins. Half of all physicians used, at most, 2 different statins for all incident prescribing, while 91.3% of physicians used, at most, 3 different statins for all of their incident prescribing. CONCLUSIONS: A high proportion of Ontario physicians issued the majority of their incident statin prescriptions for the same statin formulation. Most physicians required, at most, 3 different statins for all incident statin prescribing.
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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".