Predicting Outcomes after Hip Fracture Repair
Bibliographic record
Abstract
OBJECTIVE: To compare the activities of daily living before and after hip fracture and construct a statistical model for discharge destination and independent walking. The classification accuracy of the model was determined from an independent sample. DESIGN: Prospective study: FIM prefracture, at discharge, and at 6-mo follow-up were obtained from 63 patients who underwent operations for acute hip fractures. A statistical model for discharge destination and independent walking was made and classification accuracy was checked using 78 independent samples. RESULTS: The motor FIM scores at prefracture decreased significantly at discharge (P < 0.0001) and at 6-mo follow-up (P < 0.0001), but at 6-mo follow-up, they had increased significantly compared with those at discharge (P = 0.0103). A mobility subscale was used to predict discharge destination, and mobility and social cognition subscales were related to independent walking. The predictive accuracy was 87%. CONCLUSIONS: Motor FIM scores increase for at least 6 mos after hip fracture, and discharge destination and independent walking were highly predictable from FIM mobility and social cognition subscales.
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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 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".