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Record W4412014832 · doi:10.1186/s12891-025-08747-0

Associations between patient characteristics and delayed acute care discharge post-hip fracture surgery: a cohort study using linked health administrative data in Ontario, Canada

2025· article· en· W4412014832 on OpenAlexaffabout
Chantal Backman, Wenshan Li, Soha Shah, Steve Papp, Stephen Fung, Asnake Yohannes Dumicho, Meltem Tuna, Franciely Daiana Engel, Colleen Webber, Luke Turcotte, Daniel I. McIsaac, Paul E. Beaulé, Véronique French-Merkley, Stéphane Poitras, Benoît Lafleur, Jennifer Watt, Corita Vincent, Sharon E. Straus, Alexandre Tran, Kristen Pitzul, Sara J. T. Guilcher, Arrani Senthinathan, Peter Tanuseputro

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsMcMaster UniversityUniversity of TorontoBrock UniversityOttawa HospitalBruyèreUniversity of Ottawa
Fundersnot available
KeywordsMedicineSports medicineHip fractureOrthopedic surgeryCohortCohort studyRheumatologyEpidemiologyPhysical therapyPublic healthHealth careRehabilitationSurgeryEmergency medicineInternal medicineOsteoporosisNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Older patients frequently experience delays in discharge post-hip fracture surgery. Our study aimed to describe the sociodemographic and clinical characteristics of patients who had a surgical repair for a hip fracture and to examine the associations between these characteristics and delayed discharge (> 6 days post-surgery) for frail vs. non frail patients. METHODS: We conducted a retrospective population-based cohort study using routinely collected health administrative data housed at ICES. The study population included all individuals aged 50 to 105 years with a hip fracture who had a surgical repair in Ontario, Canada between January 1, 2015, and December 31, 2021. We used descriptive statistics and multivariable logistic regression models to characterize the association of patient socio-demographics, baseline health, and characteristics of the acute care episode with delayed discharge between non-frail and frail groups. RESULTS: We included 74,838 patients, with a mean age of 80.9 (SD 10.7) years, among which 37,234 (49.8%) had a delayed discharge. Some factors increased the odds of delayed discharge in both non-frail and frail groups included prior location in complex continuing care (non-frail OR 1.64, 95% CI 1.14,2.35, P = 0.007; frail OR 2.33, 95% CI 1.70,3.21, P < 0.0001), as well as prior residence in the community with home care (non-frail OR 9.71, 95% CI 8.89,10.6, P < 0.0001; frail OR 13.8, 95% CI 12.0,15.8, P < 0.0001), or without home care (non-frail OR 5.81, 95% CI 5.35,6.32, P < 0.0001; frail OR 4.60, 95% CI 4.14,5.11, P < 0.0001) compared to long-term care as well as residing in a neighbourhood with a higher Racialized and Newcomer Populations Index quintile (non-frail OR 1.45, 95% CI 1.37,1.55, P < 0.0001; frail OR 1.90, 95% CI 1.68,2.16, P < 0.0001). Factors that reduced the odds of delayed discharge in both non-frail and frail groups included individuals living in rural areas (non-frail OR 0.44, 95% CI 0.42,0.47, P < 0.0001; frail OR 0.41, 95% CI 0.36,0.46, P < 0.0001), or having previous fragility fractures (non-frail OR 0.44, 95% CI 0.40,0.49, P < 0.0001; frail OR 0.56, 95% CI 0.51,0.62, P < 0.0001). However, patients in the non-frail group were more likely to be delayed for the presence of comorbidities including mood or mental health conditions (OR 1.22, 95% CI 1.16,1.28, P < 0.0001), stroke (OR 1.46, 95% CI 1.28,1.67, P < 0.0001), chronic obstructive pulmonary disease (OR 1.35, 95% CI 1.24,1.47, P < 0.0001), dementia (OR 1.41, 95% CI 1.34,1.48, P < 0.0001), or diabetes (OR 1.26, 95% CI 1.20,1.32, P < 0.0001). Factors that reduced the odds of delayed discharge in the non-frail group were female sex (OR 0.87, 95% CI 0.83,0.90, P < 0.0001), or having cancer (OR 0.94, 95% CI 0.90,0.98, P = 0.0089). CONCLUSION: Delayed discharge was common after hip fracture surgery in both the non-frail and frail groups. Preoperative residential status, comorbidities and sociodemographic factors are associated with delayed discharge. These data can help to inform strategies to improve timely discharge from acute care and the overall outcomes of the older adult hip fracture population.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.325
Teacher spread0.299 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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