Bibliographic record
Abstract
BACKGROUND: Excessive prescribing of antipsychotic therapy is a concern owing to their potential to cause serious adverse events. We explored variation in the use of antipsychotic therapy across nursing homes in Ontario, Canada, and determined if prescribing decisions were based on clinical indications. METHODS: A point-prevalence study of antipsychotic therapy use in 47 322 residents of 485 provincially regulated nursing homes in December 2003. Facilities were classified into quintiles according to their mean antipsychotic prescribing rates. Residents were grouped into those with a potential clinical indication or no identified clinical indication for antipsychotic therapy. RESULTS: A total of 15 317 residents (32.4%) were dispensed an antipsychotic agent. The mean rate of antipsychotic prescribing by home ranged from 20.9% in the quintile of facilities with the lowest mean prescribing rates (quintile 1) to 44.3% in facilities with the highest mean prescribing rates (quintile 5). Compared with individuals residing in nursing homes with the lowest mean antipsychotic prescribing rates, those residing in facilities with the highest rates were 3 times more likely to be dispensed an antipsychotic agent (adjusted odds ratio [AOR], 3.0; 95% confidence interval [CI], 2.74-3.19). Similar rates were observed among residents with psychoses with or without dementia (AOR, 2.7; 95% CI, 2.35-3.09) and residents without psychoses or dementia (AOR, 2.9; 95% CI, 2.19-3.81) who had no identifiable indication for an antipsychotic therapy. CONCLUSION: Residents in facilities with high antipsychotic prescribing rates were about 3 times more likely than those in facilities with low prescribing rates to be dispensed an antipsychotic agent, irrespective of their clinical indication.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".