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Record W7036870899

Cognitive associations of benzodiazepine use in older adults

2011· article· en· W7036870899 on OpenAlexaboutno aff

Bibliographic record

VenueResearchOnline at James Cook University (James Cook University) · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionPopulationDiseaseSet (abstract data type)Identification (biology)Risk factor
DOInot available

Abstract

fetched live from OpenAlex

Use of prescription medications for various conditions is highly prevalent in older adults, often leading to the use of multiple medications. The resulting polypharmacy is widely recognized as a risk factor for many negative outcomes, but less is known about the risks of specific types of medication upon cognitive functions. Benzodiazepines are commonly prescribed for the treatment of anxiety and insomnia, among other conditions. While dependency with continued use has been the subject of much concern over this type of medication, other literature has suggested an increased risk of cognitive impairment with chronic use of benzodiazepines. The nature of the cognitive changes and the domains of cognitive function most likely to be affected have differed across various studies. Here we describe the associations between measures of various domains of cognitive functioning and benzodiazepine use in 2879 older Canadian adults from the Canadian Study of Health and Aging (CSHA; 64.3% female, mean age 81.0 years, SD=7.44). These people underwent a comprehensive medical and psychosocial evaluation that included a record of medications used, in addition to a complete personal and medical history. The CSHA was a community-based epidemiological study of the prevalence of dementia and its associated risk factors in over 10,000 Canadians. Benzodiazepines were classified according to their half-life: short (under 12 hours), medium (12 to 40 hours) or long half-life (over 40 hours); 35 elderly people were excluded since they were taking more than one class of benzodiazepine. A comprehensive neuropsychological battery that assessed the major domains of cognitive functioning was administered to all participants who completed the CSHA clinical assessment. Neuropsychological test scores for the domains of short- and long-term memory, abstract reasoning, judgement, visuoconstruction, and language formed were the primary independent variables, while gender, age, and years of education were used as covariates in logistic regression models predicting use of each class of drug. Education was not a significant covariate for any analysis. Gender proved to be a significant covariate in the case of the medium-half life drugs, but not for the other two classes. Age was a significant covariate for the majority of test scores for the short and long half-life drugs. After controlling for the covariates, the results showed a broader range of cognitive impairments with the use of short half-life benzodiazepines than with the medium half-life or the long half-life benzodiazepine compounds. Six cognitive measures, assessing abstract reasoning and comprehension, verbal fluency, verbal memory, and visuoconstruction skills (BlockDesign), showed poorer performance among those who used short half-life benzodiazepines, two measures, those of abstract reasoning and comprehension, showed impaired performance by those using medium half-life benzodiazepines, and three measures, for abstract reasoning, verbal memory, and visuoconstruction skills, showed lower performance by those taking long halflife benzodiazepines. Wechsler Similarities, the measure of abstract reasoning, was the only showing significant differences common across all three drug class models. Results are discussed in terms of both the relative extent of lower neuropsychological test scores and the context of increasing evidence of impaired functioning associated with benzodiazepine use.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.198
GPT teacher head0.341
Teacher spread0.144 · 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.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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