Short term use of antipsychotics increases the risk of serious adverse events in elderly people with dementia
Bibliographic record
Abstract
QUESTION Question: Do antipsychotics increase the risk of serious adverse events in elderly people with dementia? People: 20 682 people living in the community and 20 559 nursing home residents with a diagnosis of dementia (ICD-9 and ICD-10) and .65 years of age. All eligible individuals resident in Ontario between 1 April 1997 and 31 March 2004 were identified and their prescription data for this period obtained. Three equally sized matched groups within the community and nursing home cohorts were selected using propensity matching: atypical antipsychotic use, conventional antipsychotic use and no antipsychotic but at least one other medication prescribed (control group). Each group in the community cohort consisted of 6894 people and each group in the nursing home residents’ cohort had 6853 members. The matched groups were similar in demographic and clinical characteristics. Exclusions: history of schizophrenia, Huntington’s disease, tics, dialysis, extrapyramidal symptoms, parkinsonism, trauma, hip/ pathological fractures during 5 years prior to index prescription and those who had received palliative care prior to death. All data on participants came from four population based health care databases. Setting: Ontario, Canada; 1 April 1997–31 March 2004. Risk factors: Antipsychotic use (typical or atypical). Outcomes: Any serious adverse event resulting in hospital admission or death within 30 days of the antipsychotic prescription. Serious adverse events were defined as those resulting in death, hospitalisation or prolongation of a hospital stay, or persistent or significant disability or incapacity. ORs were adjusted for covariates such as other drugs used, hospital or doctor visits, and CT brain scans.
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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 | 0.001 | 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 | 0.001 |
| 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".