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Record W1692871678 · doi:10.1186/1471-2296-3-9

Long term benzodiazepine use for insomnia in patients over the age of 60: discordance of patient and physician perceptions

2002· article· en· W1692871678 on OpenAlexafffundabout
Leevin Mah, Ross Upshur

Bibliographic record

VenueBMC Family Practice · 2002
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchDepartment of Family and Community Medicine, University of TorontoUniversity of Toronto
KeywordsMedicineInsomniaContext (archaeology)Likert scaleFamily medicineBenzodiazepinePerceptionCross-sectional studyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to determine and compare patients' and physicians' perceptions of benefits and risks of long term benzodiazepine use for insomnia in the elderly. METHODS: A cross-sectional study (written survey) was conducted in an academic primary care group practice in Toronto, Canada. The participants were 93 patients over 60 years of age using a benzodiazepine for insomnia and 25 physicians comprising sleep specialists, family physicians, and family medicine residents. The main outcome measure was perception of benefit and risk scores calculated from the mean of responses (on a Likert scale of 1 to 5) to various items on the survey. RESULTS: The mean perception of benefit score was significantly higher in patients than physicians (3.85 vs. 2.84, p < 0.001, 95% CI 0.69, 1.32). The mean perception of risk score was significantly lower in patients than physicians (2.21 vs. 3.63, p < 0.001, 95% CI 1.07, 1.77). CONCLUSIONS: There is a significant discordance between older patients and their physicians regarding the perceptions of benefits and risks of using benzodiazepines for insomnia on a long term basis. The challenge is to openly discuss these perceptions in the context of the available evidence to make collaborative and informed decisions.

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 score0.056
Threshold uncertainty score0.344

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)0.0000.000

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 head0.033
GPT teacher head0.299
Teacher spread0.265 · 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

Citations33
Published2002
Admission routes3
Has abstractyes

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