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Record W2172294665 · doi:10.5324/nje.v22i2.1567

Benzodiazepine and z-hypnotic use in Norwegian elderly, aged 65-79

2012· article· en· W2172294665 on OpenAlexaffabout
C. Ineke Neutel, Svetlana Skurtveit, Christian Berg

Bibliographic record

VenueNorsk Epidemiologi · 2012
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineBenzodiazepineMedical prescriptionNorwegianQuarter (Canadian coin)PopulationAnxiolyticDefined daily doseHypnoticAlprazolamPsychiatryInternal medicineAnxietyPharmacologyEnvironmental health

Abstract

fetched live from OpenAlex

Purpose: Benzodiazepines/z-hypnotics (BZD-Z) guidelines suggest that elderly people ought to use anxiolytic benzodiazepines (BZD) and z-hypnotics only at low dose and only for a short time, and that hypnotic BZD not should be used at all. Since the elderly aged 65-79 tend to be recently retired but still in relatively good health, they may have different needs for BZD-Z than those older or younger. Our objective is to examine BZD-Z use in this age group.Methods: The study population consisted of Norwegians, aged 65-79, who filled prescriptions for anxiolytic BZD, hypnotic BZD and/or z-hypnotics in 2004-2009. The quantities prescribed were in daily defined doses (DDD), and 100 DDD/year was deemed excessive.Results: More than a quarter of the population received at least one BZD-Z prescription each year. Half of those received more than 100 DDD/year and a quarter received over 250 DDD/year, with these proportions increasing year by year. All three subgroups of BZD-Z showed increasing use with age and all except anxiolytic BZD showed increasing proportions of users using more than 100 DDD/year with age.Conclusions: Substantial numbers of elderly aged 65-79 receive prescriptions for BZD-Z, more with increasing age, and greater amounts per user. Guidelines are clearly ignored. While a rigid enforcement of guidelines/rules is not the answer, allowing the status quo to continue shows lack of respect for guidelines.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.017
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.282
GPT teacher head0.428
Teacher spread0.146 · 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

Citations5
Published2012
Admission routes2
Has abstractyes

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