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Record W2896080476 · doi:10.1016/j.jotr.2018.05.002

Advanced prediction of functional outcomes in hip fracture patients using premorbid functional assessments

2018· article· en· W2896080476 on OpenAlexaboutno aff
Leong-Pan Hung, Hoi-Yee Lam, Wai-Ha Fung, Kam-Tim Samuel Ngan, Chiu-tai Yip, Yim-Kwan Fung

Bibliographic record

VenueJournal of Orthopaedics Trauma and Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationHip fracturePhysical therapyMedicineBarthel indexActivities of daily livingCohortFunctional impairmentPhysical medicine and rehabilitationInternal medicineOsteoporosis

Abstract

fetched live from OpenAlex

Purpose: This historical cohort study aims to determine the relationship between premorbid functional status and functional decline in hip fracture patients. Methods: Eighty-two hip fracture patients were divided into Group A (good rehabilitation potential) and Group B (fair rehabilitation potential) based on four premorbid functional assessments: Modified Functional Ambulation Category (MFAC), Modified Barthel Index (MBI), Hong Kong Montreal Cognitive Assessment 5-Minute and functional prognosis predictive score. Declines in MFAC and MBI after rehabilitation were compared. Results: Sixty-seven percent of patients in Group A had up to one category decline in final MFAC, whereas 66% in Group B had more than one category decline. Similarly, median decline in final MBI was seven in Group A versus 22 in Group B. Conclusion: Hip fracture patients with good rehabilitation potential have significantly fewer functional declines. It is possible for them to be directly discharged from acute hospital for outpatient rehabilitation.

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 score0.014
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.308
Teacher spread0.286 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

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