A comparison of family physician and dermatologist topical corticosteroid prescriptions: A population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Topical corticosteroids (TCS) are commonly prescribed to treat inflammatory skin diseases, and appropriate prescription is necessary for treatment success. OBJECTIVE: To quantify differences between TCS prescribed by dermatologists at consultation and family physicians for patients treated for any skin condition. METHODS: Using administrative health data in Ontario, we included all Ontario Drug Benefit recipients who filled at least one TCS prescription from a dermatologist at consultation and a family physician in the year prior between January 2014 and December 2019. We estimated mean differences and 95% confidence intervals in amount (in grams) and potency between the index dermatologist prescription and the highest and most recent family physician prescription amounts and potencies in the preceding year using linear mixed-effect models. RESULTS: A total of 69,335 persons were included. The mean dermatologist amount was 34% larger than the highest amount and 54% larger than the most recent amount prescribed by family physicians. There were small but statistically significant differences in potency using established 7-category and 4-category potency classification systems. CONCLUSIONS: Compared to family physicians, dermatologists prescribed substantially larger amounts and similarly potent TCS at consultation. Further research is needed to determine the effect of these differences on clinical outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".