Predictors of Gait Speed in Patients after Hip Fracture
Bibliographic record
Abstract
PURPOSE: Following hip fracture, patients demonstrate greatly reduced walking speeds 1 year later compared with age-matched elders. The purpose of our study was to examine the factors that relate to gait speed in patients after hip fracture. METHODS: Forty-two men and women (mean age 79 +/- 7.5 years) who sustained a hip fracture participated in this study. Linear regression analysis was used to determine a statistical model that best predicted gait speed, the dependent variable. Gait speed was measured with a computerized gait mat. The independent variables were age, sex, height, weight, time post-fracture, medications, mental status, depression, balance confidence, Medical Outcome Studies, Short Form (SF-36), balance, and lower extremity isometric force. All subjects were discharged from physical therapy services, and measurements were taken, on average, 17 weeks post-fracture. RESULTS: Using stepwise regression, 72% of the variance in gait speed was explained by summed lower extremity strength normalized by body weight, general health (SF-36), and balance confidence (Activities-specific Balance Confidence Scale). CONCLUSIONS: Impairments (summed lower extremity strength) and risk factors (perception of general health and balance confidence) are important predictors of gait speed in elders after hip fracture.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".