Determinants of Neuroleptic Drug Use in Long-Term Facilities for Elderly Persons
Bibliographic record
Abstract
Neuroleptics, also called antipsychotic drugs (e.g., haloperidol, risperidone) are the cornerstone drug therapy for psychiatric disorders. Despite the fact that they are widely used in nursing homes, little is known about their clinical determinants. The goal of this cross-sectional study was to determine the prevalence rate of neuroleptic administration and to identify their determinants among 2,332 elderly residents in nursing homes. Among the residents, 649 (27.8%) had taken at least one neuroleptic drug. According to the logistic regression, the factors associated with neuroleptic drug consumption were younger age, few hours of family visits, severe cognitive impairment, insomnia, physical restraint, and disruptive behavior. In conclusion, neuroleptic drugs are administered to more than a quarter of residents in nursing homes. Alternative solutions to sleep problems and disruptive behaviors of the elderly living in long-term-care facilities should be implemented in order to reduce unnecessary use of neuroleptics.
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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) | 0.001 | 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".