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Record W2324626093 · doi:10.4140/tcp.n.2014.387

Psychotropic Drug Prescribing Survey in a Canadian Rehabilitation and Complex Care Facility

2014· article· en· W2324626093 on OpenAlexaffabout
Caroline A Warnock, Ian Ferguson

Bibliographic record

VenueThe Consultant Pharmacist · 2014
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineZopiclonePolypharmacyMedical prescriptionObservational studyAuditAntipsychoticDrug classPsychiatryPsychotropic drugEmergency medicineDrugHypnoticSchizophrenia (object-oriented programming)Intensive care medicinePharmacologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe rates of inpatient prescribing of psychotropic drugs in a rehabilitation and complex continuing care setting. DESIGN: Cross-sectional, observational study. SETTING: Providence Healthcare, Toronto, Ontario, Canada. PATIENTS: Inpatients registered in the hospital on each of four annual audit dates. INTERVENTION: An audit of medication profiles for the presence of psychotropic prescriptions, done yearly on a single day in May 2007, 2008, 2010, and 2011. MAIN OUTCOME MEASURES: The percentage of inpatients prescribed at least one antidepressant, antipsychotic, benzodiazepine, or zopiclone. RESULTS: The percentage of inpatients with at least one prescription for each class of psychotropic drug (ranging from the lowest to highest audit-year results) were as follows: any psychotropic (55% to 63%), benzodiazepines or zopiclone (31% to 40%), antidepressants (24% to 32%), antipsychotics (7% to 13%). Rates of polypharmacy within classes was highest with antidepressants, followed by benzodiazepines (including zopiclone), then antipsychotics. CONCLUSION: Despite the limitations associated with cross-sectional, observational data, rates of prescribing of psychotropic medication, apart from antipsychotics, were high. Future research will be performed to assess appropriateness of prescribing and adverse events.

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 score0.357
Threshold uncertainty score0.827

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 head0.059
GPT teacher head0.340
Teacher spread0.281 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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