Neuroleptic Drug Therapy in Older Adults Newly Admitted to Nursing Homes: Incidence, Dose, and Specialist Contact
Bibliographic record
Abstract
OBJECTIVES: To describe the incidence and dose of neuroleptic drug therapy newly dispensed for behavioral disorders to older adults admitted to nursing homes and to determine whether this use is associated with patient characteristics and contact with specialists. DESIGN: A retrospective cohort study using administrative data from a comprehensive and universal drug program. SETTING: All licensed nursing homes in Ontario, Canada. PARTICIPANTS: All 19,780 adults aged 66 and older who had no evidence of neuroleptic drug use in the previous year and no history of major psychosis and were newly admitted to a nursing home between April 1, 1998, and March 31, 2000. MEASUREMENTS: Exposure to neuroleptic drug therapy and initial dose were measured using claims submitted to the Ontario Drug Benefit Program. RESULTS: A prescription for a neuroleptic therapy was dispensed to 17% of older adults with no previous neuroleptic exposure within 100 days and to 24% within 1 year of their nursing home admission. New exposure to a neuroleptic therapy was less likely in women (odds ratio (OR)=0.7, 95% confidence interval (CI)=0.6-0.8) and more likely in residents with dementia (OR=3.5, 95% CI=3.2-3.8). Almost 10% of nursing home residents received an initial dose that exceeded recommended thresholds. Only 14% of those newly exposed had prior contact with a geriatrician or psychiatrist. CONCLUSION: Incident use of neuroleptics in Ontario nursing homes is substantial. Use of high doses suggests that some physicians may need better information about using these agents, particularly given the rapid adoption of atypical neuroleptic drug therapies.
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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 | 0.000 | 0.003 |
| 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".