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Record W4408089680 · doi:10.1016/j.jamda.2025.105521

Factors Influencing Initial Rehabilitation Type after Hip Fracture Surgery: A Retrospective Cohort Study

2025· article· en· W4408089680 on OpenAlexafffundabout
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

VenueJournal of the American Medical Directors Association · 2025
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversity of OttawaBruyèreOttawa HospitalBrock UniversityInstitut du Savoir Montfort
FundersInstitute for Clinical Evaluative SciencesImmigration, Refugees and Citizenship CanadaMinistry of HealthMinistry of Long-Term CareInstitut canadien d'information sur la santéMinistry of Health, Ontario
KeywordsMedicineRehabilitationOdds ratioRetrospective cohort studyHip fracturePopulationPhysical therapyOddsCohortCohort studyLogistic regressionOsteoporosisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe and compare the factors that impact initial rehabilitation type after hip fracture surgery. DESIGN: Retrospective population-based cohort study. SETTING AND PARTICIPANTS: People aged between 50 and 105 with a hip fracture who had a surgical repair in Ontario, Canada, between January 1, 2015, and December 31, 2021. METHODS: Descriptive statistics and a multinomial logistic regression model were used to identify factors associated with initial rehabilitation type. RESULTS: In this study, 63,401 individuals were included with a mean age of 80 years [standard deviation (SD) 10.9], mostly female (67.3%), with 86.3% living in urban areas at the time of hospitalization and most (72.6%) admitted from the community without home care. A total of 24.5% of individuals did not receive any form of rehabilitation. Rurality of residence decreased the odds of having an initial rehabilitation type in complex continuing care [odds ratio (OR), 0.23; 95% CI, 0.21-0.26], in inpatient rehabilitation (OR, 0.26; 95% CI, 0.24-0.28), or in community rehabilitation (OR, 0.54; 95% CI, 0.50-0.58) compared with no rehabilitation. Dementia decreased the odds of having an initial rehabilitation type in complex continuing care (OR, 0.75; 95% CI, 0.69-0.81), in inpatient rehabilitation (OR, 0.44; 95% CI, 0.41-0.47), or in community rehabilitation (OR, 0.88; 95% CI, 0.82-0.95) compared with receiving no rehabilitation. Previous history of fragility fracture decreased the odds of having an initial rehabilitation type in either complex continuing care (OR, 0.30; 95% CI, 0.27-0.34), in inpatient rehabilitation (OR, 0.27; 95% CI, 0.24-0.29), or in community rehabilitation (OR, 0.33; 95% CI, 0.30-0.37) compared with no rehabilitation. CONCLUSIONS AND IMPLICATIONS: Rurality of residence, dementia, and previous history of fragility fractures reduced the odds of receiving specialized inpatient rehabilitation and increased the odds of receiving no rehabilitation. Future research should focus on achieving more equitable care for individuals living in rural settings, with dementia, or with previous fragility fractures to enhance the quality of care and achieve best outcomes for the overall 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 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)0.003
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 score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.007
GPT teacher head0.308
Teacher spread0.300 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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