Manipulation under anaesthesia versus low stretch device in poor range of motion after TKA
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the effectiveness of two frequently used non-operative treatment techniques for a stiff knee after total knee arthroplasty. METHODS: Sixty-four patients with a stiff knee after total knee arthroplasty (TKA) were randomized into a manipulation under anaesthesia group, or a low load stretch (stretch) group. The patients were followed up for 6 weeks and were evaluated for maximum flexion and extension, range of motion (ROM), pain, stiffness and function. RESULTS: Both groups showed a significant increase in knee flexion in this study. Only the stretch group showed a significant increase in extension ROM. In both groups, a significant increase in Western Ontario and McMaster Universities was observed. No significant difference was observed between both groups for the flexion or extension ROM, or for any of the pain, function or stiffness scores during this study. CONCLUSIONS: The results of this study showed that the stretch technique had equal or superior results concerning ROM and function compared to manipulation under anaesthesia. The stretch technique achieved this without requiring the patient to undergo in-hospital treatment or anaesthesia, limiting the costs and the risks for complications. The results of this study showed that stretching is a valuable tool for treating joint contractures of the knee. Therefore, the use of this stretching technique may be an excellent first choice of treatment modality in patients with slow progress of knee flexion or persistent knee stiffness following TKA, prior to manipulation under anaesthesia or lysis of adhesions.
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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) | 0.001 | 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".