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Record W4288096496 · doi:10.1111/jgs.17965

Pharmacist‐driven interventions to de‐escalate urinary antimuscarinics in the Programs of All‐Inclusive Care for the Elderly

2022· article· en· W4288096496 on OpenAlexaff
Meghan Ha, Anna Furman, Sweilem B. Al Rihani, Véronique Michaud, Jacques Turgeon

Bibliographic record

VenueJournal of the American Geriatrics Society · 2022
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversité de Montréal
FundersTabula Rasa HealthCare
KeywordsMedicineAnticholinergicPharmacistMedical prescriptionDosingPsychological interventionLogistic regressionOveractive bladderDementiaObservational studyPolypharmacyBeers CriteriaEmergency medicineRetrospective cohort studyGeriatricsAdverse effectInternal medicineFamily medicinePsychiatryPharmacyAlternative medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Given associations with serious cognitive and physical adverse effects (e.g., dementia, falls), strong anticholinergics, like urinary antimuscarinics (UAMs), should be avoided in older adults. This feasibility study aimed to (1) evaluate the implementation rate of pharmacists' recommendations intended to de-escalate UAMs, (2) quantify the change in overall anticholinergic dosing exposure from these recommendations, and (3) investigate factors that predict recommendation implementation. METHODS: This was a retrospective, observational, before-and-after study. Pharmacists (n = 18) devised strategies to de-escalate UAMs in 187 participants (mean age 72.4 ± 9.4; 77.0% female; mean number of medications 12.9 ± 4.6) of 35 Programs of All-Inclusive Care for the Elderly (PACE). PACE prescribers (non-physicians and physicians) determined whether to implement recommendations. Implementation was defined as a change in the prescription records consistent with the pharmacist's recommendation at 2-, 4-, 6-, and 9-months post-recommendation. Anticholinergic dosing exposure was measured at each time point using standardized daily doses (SDD). Multivariable logistic regression was used to identify factors that predicted recommendation implementation. RESULTS: Across 9 months, recommendations were implemented in 118 out of 187 participants, yielding a 63.1% implementation rate. Of these, 77.1% (n = 91/118) implemented by month 2. Implementers' mean overall anticholinergic SDD decreased 65.4% from baseline (baseline: 2.6 [95% CI: 2.2, 3.0] to month 9: 0.9 [95% CI: 0.6,1.2], p < 0.001) whereas non-implementers demonstrated no significant change (p = 0.52). Taking <10 baseline medications (OR 2.75; 95% CI: 1.09, 7.61); baseline UAM SDD ≥2 (OR 2.20; 95% CI: 1.11, 4.44); uncomplicated recommendations (OR 3.38; 95% CI: 1.67-7.03); and baseline calcium channel blocker use (OR 2.19; 95% CI: 1.09, 4.52) predicted implementation. CONCLUSION: Our high implementation rate indicates that pharmacists' recommendations to de-escalate UAMs as a way to reduce overall anticholinergic exposure is feasible in medically complex, community-dwelling older adults. Future research should investigate whether these recommendations benefit cognitive (e.g., delirium, dementia) and/or physical functioning (e.g., falls).

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)0.006
metaresearch head score (Gemma)0.025
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.102
GPT teacher head0.443
Teacher spread0.341 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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