MétaCan
Menu
Back to cohort
Record W2610660389 · doi:10.12788/jhm.2739

High Prevalence of Inappropriate Benzodiazepine and Sedative Hypnotic Prescriptions among Hospitalized Older Adults

2017· article· en· W2610660389 on OpenAlex
Elisabeth Pek, Andrew Remfry, Ciara Pendrith, Chris Fan‐Lun, R. Sacha Bhatia, Christine Soong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Hospital Medicine · 2017
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity Health NetworkSinai Health SystemOntario Drug Policy Research NetworkWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineMedical prescriptionZopicloneZolpidemSedativeOdds ratioBenzodiazepineObservational studyHypnoticSedative/hypnoticPsychological interventionLogistic regressionRetrospective cohort studyEmergency medicineConfidence intervalAnxietyPsychiatryInsomniaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Benzodiazepines and sedative hypnotics are commonly used to treat insomnia and agitation in older adults despite significant risk. A clear understanding of the extent of the problem and its contributors is required to implement effective interventions. OBJECTIVE: To determine the proportion of hospitalized older adults who are inappropriately prescribed benzodiazepines or sedative hypnotics, and to identify patient and prescriber factors associated with increased prescriptions. DESIGN: Single-center retrospective observational study. SETTING: Urban academic medical center. PARTICIPANTS: Medical-surgical inpatients aged 65 or older who were newly prescribed a benzodiazepine or zopiclone. MEASUREMENTS: Our primary outcome was the proportion of patients who were prescribed a potentially inappropriate benzodiazepine or sedative hypnotic. Potentially inappropriate indications included new prescriptions for insomnia or agitation/anxiety. We used a multivariable random-intercept logistic regression model to identify patient- and prescriber-level variables that were associated with potentially inappropriate prescriptions. RESULTS: Of 1308 patients, 208 (15.9%) received a potentially inappropriate prescription. The majority of prescriptions, 254 (77.4%), were potentially inappropriate. Of these, most were prescribed for insomnia (222; 87.4%) and during overnight hours (159; 62.3%). Admission to a surgical or specialty service was associated with significantly increased odds of potentially inappropriate prescription compared to the general internal medicine service (odds ratio [OR], 6.61; 95% confidence interval [CI], 2.70-16.17). Prescription by an attending physician or fellow was associated with significantly fewer prescriptions compared to first-year trainees (OR, 0.28; 95% CI, 0.08-0.93). Nighttime prescriptions did not reach significance in initial bivariate analyses but were associated with increased odds of potentially inappropriate prescription in our regression model (OR, 4.48; 95% CI, 2.21-9.06). CONCLUSIONS: The majority of newly prescribed benzodiazepines and sedative hypnotics were potentially inappropriate and were primarily prescribed as sleep aids. Future interventions should focus on the development of safe sleep protocols and education targeted at first-year trainees.Journal of Hospital Medicine 2017;12:310-316.

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.

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)0.001
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 score0.116
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.272
Teacher spread0.264 · 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