Optimizing Practices, Use, Care and Services‐Antipsychotics (OPUS‐AP): A phase 2 scale‐up to 129 long‐term care (LTC) centers in Quebec, Canada
Bibliographic record
Abstract
Abstract Background Antipsychotics are often used for the first‐line management of behavioral and psychological symptoms of dementia (BPSD) despite risks and side effects, and with disregard for guidelines recommendations to prioritize non‐pharmacological interventions. OPUS‐AP builds on Canadian Foundation for Healthcare Improvement, Appropriate Use of Antipsychotic initiatives and previous work by the 4 university‐affiliated research centers on an aging population in Quebec. The OPUS‐AP strategy is supported by an evidence‐based approach, involving provincial clinical guidelines developed by Québec’s health‐technology assessment agency, the Institut national d’excellence en santé et en services sociaux (INESSS). In phase 1 of OPUS‐AP, conducted in 24 long‐term care (LTC) centers in Quebec, Canada, antipsychotic deprescribing (cessation or dose decrease) was achieved in 85,5% of residents in whom it was attempted (Cossette et al. JAMDA, 2019). Method Phase 2 of OPUS‐AP was conducted in 129 LTC centres in Quebec, Canada, from April to December 2019. OPUS‐AP aims at improving resident care through increased staff’s knowledge and competency, resident‐centered approaches, nonpharmacologic interventions, and antipsychotic deprescribing in inappropriate indications. OPUS‐AP is implemented through integrated knowledge translation and mobilization activities. Antipsychotic, benzodiazepine, antidepressant prescriptions and BPSD were evaluated every 3 months for 6 months. Result At baseline, 10,601 residents were admitted on OPUS‐AP participating wards from which 74% had a diagnosis of major neurocognitive disorder (MNCD) and 47% an antipsychotic prescription. The follow‐up cohort included 4,087 residents with both MNCD and antipsychotic prescription. Among the 1216 residents in whom antipsychotic deprescribing was attempted between baseline and 6 months and still included at 6 months, successful deprescribing was achieved in 85.6% (cessation 50.0% or dose decrease 35.6%). No increase in benzodiazepine or antidepressant prescriptions nor worsening of BPSD were observed. Conclusion Phase 2 of OPUS‐AP confirmed phase 1 results of successful antipsychotic deprescribing with scale‐up to 129 LTC centers. Phase 3 of OPUS‐AP is underway in 2020 in all of Quebec’s 341 public LTC centers.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".