Variation in Long-Term Antipsychotic Polypharmacy and High-Dose Prescribing Across Physicians and Hospitals
Bibliographic record
Abstract
OBJECTIVES: This study had two aims: to measure the prevalence of long-term prescribing of high doses of antipsychotics and antipsychotic polypharmacy in a large Canadian province and to estimate the relative contributions of patient-, physician-, and hospital-level factors. METHODS: Government hospital discharge, physician, and pharmaceutical claims data were linked to identify individuals with schizophrenia who in 2004 had antipsychotics available to them for at least 11 months. Individuals on a high dose throughout that period, as well as individuals on multiple concurrent antipsychotics (polypharmacy), were identified. Logistic and generalized linear mixed models using patient-, physician-, and hospital-level predictors were estimated. RESULTS: Among the 12,150 individuals identified, 11.9% were on a high dose and 10.4% on antipsychotic polypharmacy continually, with 3.7% in both groups. After adjustment for potential confounders, analyses showed that systematic propensity for physicians to prescribe high doses accounted for 10.9% of the remaining unexplained variance, and physicians as a group who prescribed high doses across a hospital or psychiatry department accounted for 3.0%. For antipsychotic polypharmacy the corresponding percentages were 9.7% and 6.2%. Even after adjustment, the variation in high-dose prescribing and antipsychotic polypharmacy remained substantial. CONCLUSIONS: Long-term high-dose and antipsychotic polypharmacy prescribing appeared partly driven by some physicians' and some hospitals' propensities to prescribe in this way independently of patient characteristics. Given the weight of the evidence against high-dose prescribing and antipsychotic polypharmacy, measures addressed to physicians and hospitals most likely to prescribe high doses, antipsychotic polypharmacy, or both should be considered.
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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".