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Record W2907516894 · doi:10.9778/cmajo.20180121

Benzodiazepine receptor agonist dispensations in Alberta: a population-based descriptive study

2018· article· en· W2907516894 on OpenAlexaffvenueabout
Daniala L. Weir, Salim Samanani, Fizza Gilani, Ed Jess, Dean T. Eurich

Bibliographic record

VenueCMAJ Open · 2018
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsAlliance for Canadian Health Outcomes Research in DiabetesCollege of Physicians and Surgeons of OntarioMcGill University Health CentreUniversity of Alberta
Fundersnot available
KeywordsMedicinePopulationDemographyDescriptive statisticsEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: There is increasing concern over the use of benzodiazepine receptor agonists (BZRAs). The objective of this study was to describe BZRA dispensations in the province of Alberta in 2015 according to age, sex and appropriateness. METHODS: A population-based descriptive study of people 10 years of age or older with at least 1 BZRA dispensation in Alberta, Canada, between Jan. 1 and Dec. 31, 2015, was conducted. Prevalence of BZRA use, characteristics of BZRAs dispensations, use at the individual level and appropriateness were determined. RESULTS: value for trend < 0.001) and was consistently highest among females. Twenty percent of patients used both Z-drugs and benzodiazepines. BZRA users had an average of 7 dispensations (standard deviation [SD] 20), 137 days of use overall (SD 123) and a maximum period of consecutive use of 90 days (SD 95). Days of consecutive use were highest among those aged 65 years or older (126 d). A total of 62 795 (17%) people used more than 1 distinct BZRA ingredient concurrently and 10% had 3 or more distinct prescribers. INTERPRETATION: The prevalence of BZRA use was high and a substantial proportion of use appeared to be potentially inappropriate. This study supports the need for continued monitoring for the prescribing and use of these medications at the population level.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0070.002

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 head0.038
GPT teacher head0.340
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2018
Admission routes3
Has abstractyes

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