SPECIFICITY OF FUNCTIONAL MOBILITY MEASURES IN OLDER ADULTS FOLLOWING HIP FRACTURE
Bibliographic record
Abstract
Functional outcome measures are used to assess activities of daily living and global function. Specificity of outcome measures is important when assessing response to a treatment intervention. PURPOSE To determine relationships between two measures of functional mobility [Timed Up and Go test (TUG) and the Self-Paced Walking test (SPW)], and a measure of overall functional status [Functional Independence Measure (FIM™)] and the motor component of the FIM™ [mobility (transfers: bed/chair/wheelchair, toilet, tub/shower) and locomotion: (walk/wheelchair, stair climbing)] in patients admitted to a rehabilitation hospital following hip fracture. METHODS The TUG, SPW, and FIM™ were administered within 24h of admission and 24h before discharge. Data were collected on 38 fracture patients (7 men, 31 women; mean age of sample 79±6 y, range 64–90 y) who attended daily physiotherapy and occupational therapy sessions during a mean length of stay (LOS) of 29±7 days. RESULTS There was a significant (p < .01) increase in function from admission to discharge in TUG (49±25s vs 32±16s), SPW (.36±.21 m/s vs .51±.18 m/s), motor FIM™ (13±4 vs 25±3), and global FIM™ (83±12 vs 108±6) tests. Significant correlations on admission were found between motor FIM™ and SPW scores (r=.47, p < .05), motor FIM™ and TUG (r = −.36, p < .05) scores, and global FIM™ and TUG scores (r = −.35, p < .05). However, no significant correlations were found for either change or discharge scores between motor FIM™ or global FIM™ scores and the two functional mobility measures. CONCLUSION The global FIM™ and motor FIM™ may not be specific measures of functional mobility following rehabilitation in patients with hip fracture. Supported by the Parkwood Hospital Foundation (1999– 2001).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".