Associations between gait analysis parameters and patient satisfaction one year following primary total knee arthroplasty
Bibliographic record
Abstract
Background: The purpose of this prospective study was to understand the relation between gait outcomes and patient satisfaction one year after total knee arthroplasty (TKA). Methods: Seventy-nine patients were evaluated before and one year after TKA using clinical gait analysis. Specific gait outcomes were analyzed: gait speed, stance phase, range of motion (ROM) knee flexion and maximal knee flexion. The parameters of interest selected for the statistical analysis were: gait speed and maximal knee flexion during gait. The Western Ontario and MacMaster Osteoarthritis Index (WOMAC) and patient satisfaction were also assessed. The satisfaction was evaluated using a questionnaire and was splited in five categories: very unsatisfied, unsatisfied, neutral, satisfied or very satisfied. To assess associations between patient satisfaction and maximal knee flexion during gait and gait speed, an unadjusted ordinal logistic regression analysis was used. The analysis was then adjusted for covariates: age and Body Mass Index (BMI) before surgery and WOMAC pain one year after surgery. Results: All gait outcomes after TKA had significantly improved. The ordinal logistic regression analysis found significant associations between patient satisfaction and maximal knee flexion after TKA (unadjusted and adjusted) but not for gait speed. Conclusion: These findings show that all patients improved their gait outcomes one year after TKA but only a higher maximal knee flexion during gait may influence the level of patient satisfaction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".