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Record W1776110158 · doi:10.1186/1471-2318-1-4

High daily doses of benzodiazepines among Quebec seniors: prevalence and correlates

2001· article· en· W1776110158 on OpenAlexafffundabout
Mary Egan, Christina Wolfson, Yola Moride, Johanne Monette

Bibliographic record

VenueBMC Geriatrics · 2001
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsJewish General HospitalUniversité de MontréalMcGill UniversityUniversity of Ottawa
FundersAustralian GovernmentHealth CanadaUniversity of Ottawa
KeywordsMedicineMedical prescriptionAnxietyActivities of daily livingCognitive impairmentRehabilitationCognitionFamily medicineEmergency medicinePsychiatryPhysical therapyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Use of high daily doses of benzodiazepines is generally contraindicated for seniors. While both patient and physician factors may influence the use of high daily doses, previous research on the effect of patient factors has been extremely limited. The objectives of this study were to determine the one year prevalence of use of high daily doses of benzodiazepines, and examine physician and patient correlates of such use among Quebec community-dwelling seniors. METHODS: Patient information for 1423 community-dwelling Quebec seniors who participated in the Canadian Study of Health and Aging was linked to provincial health insurance administrative data bases containing detailed information on prescriptions received and prescribers. RESULTS: The standardized one year period prevalence of use of high daily doses of benzodiazepines was 7.9%. Use of high daily doses was more frequent among younger seniors and those who had reported anxiety during the previous year. Patients without cognitive impairment were more likely to receive high dose prescriptions from general practitioners, while those with cognitive impairment were more likely to receive high dose prescriptions from specialists. CONCLUSION: High dose prescribing appears to be related to both patient and physician factors.

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 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 score0.714

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)0.0010.000

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 head0.010
GPT teacher head0.248
Teacher spread0.237 · 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

Citations18
Published2001
Admission routes3
Has abstractyes

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