Specificity of Functional Mobility Measures in Older Adults After Hip Fracture
Bibliographic record
Abstract
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".