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Record W3011770090 · doi:10.5770/cgj.23.419

Canadian Guidelines on Benzodiazepine Receptor Agonist Use Disorder Among Older Adults

2020· article· en· W3011770090 on OpenAlexafffundvenueabout
David Conn, David B. Hogan, Lori Amdam, Keri-Leigh Cassidy, Peter Cordell, Christopher Frank, David M. Gardner, Morris Goldhar, Joanne M-W Ho, Christopher Kitamura, Nancy Vasil

Bibliographic record

VenueCanadian Geriatrics Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité de MontréalQueen's UniversityMcMaster UniversityDalhousie UniversityUniversity of TorontoHealth Sciences NorthBaycrest HospitalCanadian Mental Health AssociationUniversity of Calgary
FundersHealth CanadaUlster UniversityU.S. Department of Justice
KeywordsMedicineBenzodiazepinePsychiatryInsomniaAnxietyAgonist

Abstract

fetched live from OpenAlex

BACKGROUND: Benzodiazepine receptor agonist (BZRA) use disorder among older adults is a relatively common and challenging clinical condition. METHOD: The Canadian Coalition for Seniors' Mental Health, with financial support from Health Canada, has produced evidence-based guidelines on the prevention, identification, assessment, and management of this form of substance use disorder. RESULTS: Inappropriate use of BZRAs should be avoided by considering non-pharmacological approaches to the management of late life insomnia, anxiety, and other common indications for the use of BZRA. Older persons should only be prescribed BZRAs after they are fully informed of alternatives, benefits, and risks associated with their use. Clinicians should have a high index of suspicion for the presence of BZRA use disorders. The full version of these guidelines can be accessed at www.ccsmh.ca. CONCLUSIONS: A person-centred, stepped care approach utilizing gradual dose reductions should be used in the management of BZRA use disorder.

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)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.137
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.006
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0230.007

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations33
Published2020
Admission routes4
Has abstractyes

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