[P2–022]: OPTIMIZING PRESCRIBING OF ANTIPSYCHOTICS IN LONG‐TERM CARE (OPAL)
Bibliographic record
Abstract
Increasing numbers of older adults are affected by dementia, and many will eventually reside in long-term care (LTC). Inappropriate antipsychotic use in this setting is common and associated with serious adverse effects. Limited evidence exists on the most effective strategies for reducing inappropriate antipsychotic prescribing. The objective of the study was to evaluate a multicomponent approach including an educational program to reduce inappropriate antipsychotic prescribing in LTC. A prospective, stepped wedge, controlled study design was used to evaluate the effect of the intervention in 10 LTC facilities in Ontario and Saskatchewan, Canada. The primary outcome was the proportion of residents receiving an antipsychotic without a diagnosis of psychosis. At baseline, the overall antipsychotic prescribing rate was 28.6% (Standard Deviation (SD) 4.3%). Data collection is ongoing; results at three months following implementation showed a relative reduction in the mean rate of inappropriate antipsychotic prescribing of 5.2% (SD 7.8%). The change was not statistically significant (P=0.06). There were no significant changes in related quality indicators, including falls, restraint use, and behavioural worsening. Preliminary study results show a trend towards lower rates of inappropriate antipsychotic prescribing. The intervention may offer a sustainable and practical means by which to improve the care of older adults in LTC.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".