Continuous passive motion and its effects on knee flexion after total knee arthroplasty in patients with knee osteoarthritis
Bibliographic record
Abstract
PURPOSE: This study evaluated the effects of continuous passive motion (CPM) on accelerated flexion after total knee arthroplasty (TKA) and whether CPM application measures (i.e. initial angle and daily increment) are associated with functional outcomes. METHODS: A retrospective investigation was conducted at the rehabilitation centre of a university-based teaching hospital. Patients who received CPM therapy immediately after TKA surgery were categorized into rapid-, normal-, and slow-progress groups according to their response to CPM during their acute inpatient stay. Knee pain, passive knee flexion, and knee function-measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)-were assessed preoperatively at discharge and at 3- and 6-month outpatient follow-up visits. RESULTS: A total of 354 patients were followed for 6 months after inpatient-stay discharge. The patients in the rapid-progress group (n = 119) exhibited significantly greater knee flexions than those in the slow-progress group did (n = 103) at the 3-month follow-up [mean difference (MD) = 10.3°, 95 % confidence interval (CI) 4.3°-16.3°, p < 0.001] and 6-month follow-up (MD = 10.9°, 95 % CI 6.3°-15.6°, p < 0.001). Significant WOMAC score differences between the rapid- and slow-progress groups were observed at the 3-month follow-up (MD = 7.2, 95 % CI 5.4-9.1, p < 0.001) and 6-month follow-up (MD = 16.1, 95 % CI 13.4-18.7, p < 0.001). CPM initial angles and rapid progress significantly predicted short- and long-term outcomes in knee flexion and WOMAC scores (p < 0.001). CONCLUSION: When CPM is used, early application with initial high flexion and rapid progress benefits knee function up to 6 months after TKA. LEVEL OF EVIDENCE: II.
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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 | 0.000 | 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) | 0.001 | 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".