Preoperative predictors of locomotor ability two months after total knee arthroplasty for severe osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To identify preoperative predictors of locomotor ability 2 months after total knee arthroplasty (TKA). METHODS: Sixty-five participants scheduled for a first TKA were recruited. The dependent variable was the distance covered during the 6-minute gait test 2 months after TKA. The independent variables, measured before surgery, were grouped into 3 categories: 1). personal; 2). organic system, and 3). capability variables. Multiple regression analyses were conducted to determine the best predictors of the dependent variable. Modified cross-validation of the model combining predictors from the three categories was obtained with 10 random samples derived from the original cohort by resampling with replacement. RESULTS: Predictors were identified in the 3 categories: 1). gender, number of comorbidities, body mass index, 2). knee pain, flexion and strength, lower limb mechanical power, and 3). preoperative 6-minute gait distance. When these variables were modeled together only the preoperative 6-minute gait distance and knee pain and flexion remained significant (adjusted R(2) = 0.66). CONCLUSION: Using variables easily measured before surgery, it is possible to predict with good accuracy locomotor ability 2 months after TKA.
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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 | 0.004 |
| 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 | 0.001 |
| 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".