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Record W4361282859 · doi:10.1016/j.physio.2023.03.002

Frequency, duration, and type of physiotherapy in the week after hip fracture surgery – analysis of implications for discharge home, readmission, survival, and recovery of mobility

2023· article· en· W4361282859 on OpenAlexaff
Orouba Almilaji, Salma Ayis, Aïcha Goubar, Lauren A Beaupré, Ian D. Cameron, Rhian Milton-Cole, Celia L. Gregson, Antony Johansen, Morten Tange Kristensen, Jay Magaziner, Finbarr C. Martin, Catherine Sackley, Euan Sadler, Toby O. Smith, Boris Sobolev, Katie Jane Sheehan

Bibliographic record

VenuePhysiotherapy · 2023
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
FundersNational Institute for Health Research Applied Research Collaboration WestGuy's and St Thomas' NHS Foundation TrustNational Institutes of HealthVersus ArthritisNational Institute for Health and Care ResearchManchester Biomedical Research CentreNational Health Research InstitutesKing's College LondonUK Research and InnovationDepartment of Health and Social CareChartered Society of Physiotherapy Charitable Trust
KeywordsHip fractureMedicineDuration (music)Physical therapyPhysical medicine and rehabilitationInternal medicineOsteoporosis

Abstract

fetched live from OpenAlex

Purpose To examine the association between physiotherapy access after hip fracture and discharge home, readmission, survival, and mobility recovery. Methods A 2017 Physiotherapy Hip Fracture Sprint Audit was linked to hospital records for 5383 patients. Logistic regression was used to estimate the association between physiotherapy access in the first postoperative week and discharge home, 30-day readmission post-discharge, 30-day survival and 120-days mobility recovery post-admission adjusted for age, sex, American Society of Anesthesiology grade, Hospital Frailty Risk Score and prefracture mobility/residence. Results Overall, 73% were female and 40% had high frailty risk. Patients who received ≥2 hours of physiotherapy (versus less) had 3% (95% Confidence Interval: 0–6%), 4% (2–6%), and 6% (1–11%) higher adjusted probabilities of discharge home, survival, and outdoor mobility recovery, and 3% (0–6%) lower adjusted probability of readmission. Recipients of exercise (versus mobilisation alone) had 6% (1–12%), 3% (0–7%), and 11% (3–18%) higher adjusted probabilities of discharge home, survival, and outdoor mobility recovery, and 6% (2–10%) lower adjusted probability of readmission. Recipients of 6–7 days physiotherapy (versus 0–2 days) had 8% (5–11%) higher adjusted probability of survival. For patients with dementia, improved probability of survival, discharge home, readmission and indoor mobility recovery were observed with greater physiotherapy access. Conclusion Greater access to physiotherapy was associated with a higher probability of positive outcomes. For every 100 patients, greater access could equate to an additional eight patients surviving to 30-days and six avoiding 30-day readmission. The findings suggest a potential benefit in terms of home discharge and outdoor mobility recovery. Contribution of the Paper •To substantiate a case for additional physiotherapy, evidence for an association with improved outcomes is needed. •Analysis of 5383 patients suggests greater access to physiotherapy was associated with higher probability of positive outcomes. •For every 100 patients, this could equate to six more patients avoiding 30-day readmission and eight more patients surviving to 30-days. •The association between access to physiotherapy and survival persisted irrespective of dementia diagnosis. •For other outcomes, associations varied by the presence/absence of dementia and should be explored by future cohort studies.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.356
Teacher spread0.323 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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