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Record W4393358373 · doi:10.1080/02703181.2024.2322488

Rehabilitation Therapy and Multimorbidity: A Retrospective Cohort Study

2024· article· en· W4393358373 on OpenAlexafffund
Amanda Mofina, Jordan Miller, Joan Tranmer, Wenbin Li, Catherine Donnelly

Bibliographic record

VenuePhysical & Occupational Therapy In Geriatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsQueen's UniversityUniversity of Waterloo
FundersMinistry of Long-Term CareInstitute for Clinical Evaluative SciencesMinistry of Health, Ontario
KeywordsRehabilitationRetrospective cohort studyMedicinePhysical therapyPhysical medicine and rehabilitationCohortOccupational therapyInternal medicine

Abstract

fetched live from OpenAlex

Aims Home care rehabilitation therapists can address the functional health needs of those with multimorbidity. This study described individuals with multimorbidity receiving home care rehabilitation therapy and examined the relationship between receipt of rehabilitation therapy and hospital utilization.Methods The cohort included long-stay home care clients experiencing multimorbidity who were discharged from an inpatient rehabilitation setting (N = 5,234) between 2007 and 2015. Multivariable logistic regression was used to examine the association between receipt of rehabilitation therapy and hospitalization.Results Nearly 40% of this cohort had 5+ chronic conditions. Those receiving only rehabilitation therapy were less likely to be readmitted to the hospital (OR = 0.68; 95% CI: 0.51–0.92), and less likely to use emergency services (OR = 0.80; 95% CI: 0.66–0.97) at the 3-month timeframe. Similar trends were observed at the 12-month timeframe.Conclusions Individuals with multimorbidity have complex health needs and home care rehabilitation therapists aid in sustained discharges and influence subsequent health utilization.

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 score0.016
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.053
GPT teacher head0.384
Teacher spread0.331 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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