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Specificity of Functional Mobility Measures in Older Adults After Hip Fracture

2003· article· en· W2022053665 on OpenAlexaff
Marissa Mendelsohn, Daniel S. Leidl, Tom J. Overend, Robert J. Petrella

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2003
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsCanadian Armed ForcesParkwood InstituteLawson Health Research Institute
Fundersnot available
KeywordsBerg Balance ScaleMedicineHip fractureRehabilitationBalance (ability)Physical therapyPhysical medicine and rehabilitationInternal medicineOsteoporosis

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the relationships between measures of functional mobility (Timed Up and Go [TUG], Self-Paced Walking [SPW], Berg Balance Scale [BBS]) and global functional status (FIM trade mark instrument), the motor component of the FIM instrument (motor FIM), and the mobility/locomotor-specific FIM component (ML-FIM) in older patients admitted to an inpatient rehabilitation program after hip fracture. DESIGN: The TUG, SPW, BBS, and FIM instrument were administered within 24 hr after admission and before discharge to 20 patients undergoing inpatient rehabilitation after a hip fracture. RESULTS: Significant correlations at admission were found between FIM and TUG scores (r = -0.47; p < 0.05), TUG and motor FIM (r = -0.45; p < 0.05), TUG and ML-FIM (r = -0.58; p < 0.01), FIM and BBS (r = 0.60; p < 0.01), motor FIM and BBS (r = 0.50; p < 0.05), and ML-FIM and BBS (r = 0.45; p < 0.05). At discharge, a significant correlation was found between the motor FIM and SPW (r = -0.49; p < 0.05). Change scores between both the motor FIM and ML-FIM and TUG scores were significantly correlated (r = -0.47, p < 0.05, r = -0.50, p < 0.05, respectively). CONCLUSIONS: The FIM instrument, motor FIM, and ML-FIM may not be specific measures of functional mobility in patients with hip fracture.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.440
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.268
Teacher spread0.262 · 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

Citations32
Published2003
Admission routes1
Has abstractyes

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