MétaCan
Menu
← Back to cohort
Record W3111005472 · doi:10.1002/alz.037171

Optimizing Practices, Use, Care and Services‐Antipsychotics (OPUS‐AP): A phase 2 scale‐up to 129 long‐term care (LTC) centers in Quebec, Canada

2020· article· en· W3111005472 on OpenAlexaffabout
Marie‐Andrée Bruneau, Yves Couturier, Suzanne Gilbert, Diane Boyer, Jacques Ricard, Tanya MacDonald, Marcel Arcand, Michèle Morin, Marilyn Tousignant, Andrée‐Anne Rhéaume, Jean‐Philippe Turcotte, Benoît Cossette

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de SherbrookeUniversité LavalUniversité de MontréalCentre Hospitalier Universitaire de SherbrookeMinistère de la Santé et des Services Sociaux (Québec)Canadian Foundation for Healthcare ImprovementInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsAntipsychoticDeprescribingMedicineMedical prescriptionPsychiatryPopulationOpusDementiaGerontologyFamily medicinePolypharmacyNursingSchizophrenia (object-oriented programming)Environmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.761

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.007
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.003
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0040.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.033
GPT teacher head0.332
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicDementia and Cognitive Impairment Research→French-language works237,207→