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Record W2729276698 · doi:10.1093/geroni/igx004.2526

ANTIPSYCHOTIC STEWARDSHIP FOR OLDER PATIENTS IN ACUTE CARE: PROMOTING APPROPRIATE PRESCRIBING

2017· article· en· W2729276698 on OpenAlexaff
D. Brown, Mireille Norris, Dov Gandell, R. Jaunkalns, Jovita Ortiz Contreras, B.A. Liu

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsOntario Stroke NetworkHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsAntipsychoticMedicineQuetiapineStewardship (theology)Medical prescriptionAuditDeliriumAdverse effectHaloperidolIntensive care medicinePsychiatryNursingPharmacologySchizophrenia (object-oriented programming)Internal medicine

Abstract

fetched live from OpenAlex

Antipsychotics are associated with significant adverse effects and evidence to guide the choice of specific antipsychotic and the appropriate dose in older acutely ill patients is not robust. Our stewardship program is a novel approach to improve appropriateness of antipsychotics in acute care. In addition to the antipsychotic review, the stewardship team offers case-based learning sessions and integrates best practices in delirium management with front-line staff. Other antipsychotic stewardship programs have focused on audit and feedback of prescribing patterns. In contrast, our approach has been to provide education and reinforce non-pharmacologic strategies to behaviour management as an alternative or adjunct to antipsychotic use. In preliminary analysis, 55% of patients receiving antipsychotics were male and 77% of orders were for new prescriptions. Quetiapine and haloperidol were the most frequently ordered antipsychotics at 43% and 41% of orders, respectively. We discontinued or decreased the dose of antipsychotic in 62% of orders.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.040
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

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

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 head0.114
GPT teacher head0.416
Teacher spread0.303 · 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 teacher head, 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
Published2017
Admission routes1
Has abstractyes

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