Ergometer Cycling After Hip or Knee Replacement Surgery
Bibliographic record
Abstract
BACKGROUND: The optimal treatment strategy following primary total hip or knee replacement remains unknown. The purpose of this study was to evaluate the effect of ergometer cycling after hip or knee replacement surgery on health-related quality of life and patient satisfaction. METHODS: Three hundred and sixty-two patients were randomly assigned to either perform or not perform ergometer cycling beginning two weeks after total hip or knee replacement. The primary outcome was self-reported physical function as measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at three, six, twelve, and twenty-four months postoperatively. Results were compared with published thresholds for minimal clinically important improvements. RESULTS: The baseline characteristics of the two groups were similar. After the hip arthroplasties, all of the outcome parameters were superior in the ergometer cycling group at all follow-up intervals, and the primary outcome, physical function as measured with the WOMAC, was significantly better at three months (21.6 compared with 16.4 points, effect size = 0.33, p = 0.046) and twenty-four months (14.7 compared with 9.0 points, effect size = 0.37, p = 0.019). After the hip arthroplasties, a higher percentage of the ergometer cycling group was "very satisfied" at all follow-up intervals (for example, 92% compared with 80% at three months; p = 0.027). The significant differences in the primary outcome exceeded the absolute minimal clinically important improvement threshold by a factor of 2.0. No significant differences between the study groups were seen after the knee arthroplasties. CONCLUSIONS: Ergometer cycling after total hip arthroplasty is an effective means of achieving significant and clinically important improvement in patients' early and late health-related quality of life and satisfaction. However, this study does not support the use of ergometer cycling after 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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".