MétaCan
Menu
Back to cohort
Record W2138365859 · doi:10.4212/cjhp.v64i5.1066

Medications Prescribed and Occurrence of Falls in General Medicine Inpatients

2011· article· en· W2138365859 on OpenAlexaffvenue
Richard P Cashin, Meiti Yang

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2011
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsMedicineMedical prescriptionPharmacyEmergency medicineFall preventionIncident reportRetrospective cohort studyInjury preventionPoison controlPediatricsInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

Background: Although falls are multifactorial, medications are a key risk factor that may be modifiable. Falls were among the most common occurrences entered into a risk identification system at the authors’ hospital.Objectives: To identify whether general medicine inpatients who had experienced a fall were taking any medications known to be associated with falls.Methods: The literature was reviewed to develop a list of high-risk medications that have been associated with falls. In a retrospective quality-improvement database-based study, information from the risk identification system was merged with data from the pharmacy dispensing system for general medicine inpatients who had experienced a fall. The primary end point was the percentage of patients with a documented fall who had a prescription for a high-risk medication. The number of such medications that had been prescribed for patients who fell was also calculated.Results: Eighty-one unique medications were found to be associated with falls. During the study period (April 1, 2008, to March 31, 2009), 151 patients experienced a fall. Of those, 144 (95.4%) were taking at least one high-risk medication. The mean number of high-risk medications per patient who experienced a fall was 2.2. Of all documented falls, a new high-risk medication had been started within 7 days before the fall for 74 (49.0%) and within 24 h before the fall for 17 (11.3%). The most commonly prescribed drugs during all time periods (i.e., within 24 h or 7 days before the fall or since the patient’s admission) were lorazepam and zopiclone. The pharmacy database did not track administration of medications, so it is possible that some of the drugs prescribed were not actually taken by the patient.Conclusion: Almost all inpatients who experienced a fall during the hospital stay had a prescription for at least one medication associated with a high risk for falls. Lorazepam and zopiclone were the drugs most commonly associated with falls in this hospital, and their use should be reviewed.RÉSUMÉContexte : Bien que la cause des chutes soit multifactorielle, les médicaments en sont un facteur de risque clé pouvant être modifié. Les chutes sont l’un des événements les plus fréquents saisis dans le système d’identification des risques à l’hôpital des auteurs.Objectifs : Déterminer si les patients hospitalisés en médecine générale qui ont subi une chute prenaient des médicaments reconnus pour être associés aux chutes.Méthodes : On a effectué une revue de la littérature pour établir une liste des médicaments à haut risque associés aux chutes. Une étude rétrospective fondée sur une base de données visant l’amélioration de la qualité a fusionné les données tirées du système d’identification des risques aux données issues du système de distribution de la pharmacie portant sur les patients hospitalisés en médecine générale ayant subi une chute. Le paramètre d’évaluation principal était le pourcentage de patients ayant subi une chute constatée à qui l’on avait prescrit un médicament à haut risque. Le nombre de médicaments à haut risque ayant été prescrits aux patients qui avaient subi une chute a également été calculé.Résultats : On a dénombré 82 médicaments différents associés à des chutes. Durant la période de l’étude (du 1er avril 2008 au 31 mars 2009), 151 patients ont subi une chute. De ceux-ci, 144 (95,4 %) prenaient au moins un médicament à haut risque. Le nombre moyen de médicaments à haut risque par patient ayant subi une chute était de 2,2. De tous les cas de chutes consignés, on avait commencé à administrer un nouveau médicament à haut risque dans les sept jours précédant la chute chez 74 (49,0 %) des patients et dans les 24 heures avant la survenue de la chute chez 17 (11,3 %) des patients. Les médicaments les plus souvent prescrits pour toutes les périodes de temps (c.-à-d. moins de 24 heures ou de sept jours avant la chute ou depuis l’admission du patient) étaient le lorazépam et la zopiclone. La base de données de la pharmacie ne permet pas de savoir si les médicaments ont été réellement administrés et il est donc possible que certains des médicaments prescrits n’aient pas été pris par quelques patients.Conclusion : Presque tous les patients ayant subi une chute durant leur hospitalisation avaient reçu une ordonnance d’au moins un des médicaments associés à un risque élevé de chute. Le lorazépam et la zopiclone étaient les médicaments les plus souvent associés à des chutes dans cet hôpital et leur utilisation devrait donc être examinée.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.367
Teacher spread0.310 · 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 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

Citations25
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of Hospital PharmacySame topicBalance, Gait, and Falls PreventionFrench-language works237,207