The Relationship Between Variations in Antipsychotic Prescribing Across Nursing Homes and Short-Term Mortality
Bibliographic record
Abstract
BACKGROUND: High rates of antipsychotic drug prescribing in nursing homes can signal poor quality processes, but also raise concerns about drug safety due to the mortality risk of this therapy. Determining the extent to which variations in antipsychotic use are a symptom of facility-level quality problems as compared with a drug safety issue is important for selecting the correct interventions to effect change. OBJECTIVE: To determine whether nursing homes with higher rates of antipsychotic dispensing had higher rates of short-term mortality among their residents. METHODS: This population-based study examined 60,105 older adults newly admitted to nursing homes in Ontario between April 1, 2000 and March 31, 2004. Using adjusted Cox proportional hazard models, we explored the relationship between facility-level dispensing rates and mortality, controlling for resident characteristics. Facilities were grouped into quintiles according to mean antipsychotic rate. All-cause mortality at 30 and 120 days after admission was stratified by recent hospital discharge and analyzed by quintile. RESULTS: Average antipsychotic dispensing ranged from 11.6% in the lowest quintile to 30.0% in the highest. Among residents with no recent hospitalization, all-cause mortality at 30 days was 2.5% in the lowest compared with 3.3% in the highest quintile (adjusted hazard ratio: 1.28, confidence interval: 1.06-1.56) and at 120 days was 9.3% compared with 11.7% (adjusted hazard ratio: 1.25, confidence interval: 1.13-1.39). CONCLUSION: Residents were at increased risk of death simply by being admitted to a facility with a higher intensity of antipsychotic drug use, despite similar clinical characteristics at admission.
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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 | 0.000 | 0.001 |
| 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".