Use of physician profiles to influence prescribing of topical corticosteroids.
Bibliographic record
Abstract
BACKGROUND: Physician profiling is a tool used to attempt to affect changes in prescribing. The Drug Evaluation Alliance of Nova Scotia (DEANS) decided to implement a physician profiling project to determine if prescribing of topical corticosteroids could be altered. OBJECTIVES: To evaluate a DEANS initiative utilizing physician prescribing profiles to shift prescribing of topical corticosteroids from higher to lower potency agents in beneficiaries of the Nova Scotia Seniors' Pharmacare Program. METHODS: Administrative claims from the Nova Scotia Seniors' Pharmacare program were used to identify prescriptions for topical corticosteroids. Prescriptions were summarized at the individual physician level, and aggregated by Anatomical Therapeutic Classification into weak, moderately potent, potent and very potent products. The number of prescriptions for topical corticosteroids was compared for the twelve-month period before and after mailing of the profiles. Overall results were aggregated by utilization and expenditures. RESULTS: The number of prescriptions for topical corticosteroids per physician profiled was 44.0 in 2000/2001 and 42.8 in 2001/2002 (p = NS) and the expenditures per physician profiled were 838.94 dollars in 2000/2001 and 826.81 dollars in 2001/2002 (p = NS). There was a small decrease in prescriptions dispensed for potent topical products over the profiling period (52.4% of prescriptions in 2000/2001 versus 51.5% of prescriptions in 2001/2002, p=0.03). Otherwise, changes in utilization or expenditures for topical corticosteroids were not statistically different between the profiling periods. CONCLUSIONS: This project showed that mailing unsolicited individual-level profiles did not alter prescribing or expenditures for topical corticosteroids over a two-year period. Further work is needed to determine physician attitudes towards such projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".