Preoperative physical factors that predict stair-climbing ability at one month after total knee arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To identify preoperative physical performance factors that predict stair-climbing ability at 1 month after total knee arthroplasty. DESIGN: Prospective cohort study. SETTING: University-based rehabilitation centre. SUBJECTS: Eighty-four patients who underwent a primary unilateral total knee arthroplasty Methods: Before and 1 month post-operation, the patients completed physical performance tests, including a stair-climbing test, a 6-minute walk test, a Timed Up-and-Go test, tests of the isometric flexor and extensor strength of the operated and non-operated knees, and instrumental gait analysis. Disease-specific physical function was measured by the Western Ontario McMaster Universities Osteoarthritis Index. RESULTS: Correlation analysis showed that postoperative stair-climbing test scores were significantly correlated with preoperative physical performance and function. Linear regression analysis showed that postoperative stair-ascent scores were correlated with preoperative Timed Up-and-Go test scores and peak torque of the extensor of the operated knee. Postoperative stair-descent scores were positively correlated with preoperative stair-descent scores and age. CONCLUSION: The results show that preoperative balance ability and quadriceps strength in the operated knee could influence postoperative stair-climbing ability at 1 month after total knee arthroplasty. These findings will be useful for developing pre- and post-operative rehabilitation strategies for improving stair-climbing ability in the early stages after total knee arthroplasty.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".