Manipulation under anaesthetic following total knee arthroplasty: Predicting stiffness and outcome
Bibliographic record
Abstract
PURPOSE: A stiff total knee replacement can severely limit a patient's post-operative function, but there remain few prospective trials identifying those patients at risk, nor the efficacy of manipulation. We analysed our prospectively collected database to assess predictors of stiffness and outcomes following manipulation. METHODS: Using prospectively collected knee arthroplasty data, including preoperative and post-operative range of knee movement, SF-12 (physical and mental) and The Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores, patients requiring manipulation were compared to a matched group of patients not requiring manipulation, with a detailed statistical analysis undertaken to assess potential risk factors and the post-manipulation outcome. RESULTS: Of the 1313 arthroplasty patients, 69 required manipulation. Patients with less than 80° of flexion at discharge, diabetes or on warfarin were more likely to require manipulation, but flexion at discharge was the overwhelming predictive factor for stiffness. Forty per cent of the range of movement gained during manipulation was maintained at 1 year, with earlier manipulation deriving greater improvements. While the WOMAC scores improved post-manipulation, there was no significant difference in either of the SF12 scores. CONCLUSION: Flexion at discharge is the overwhelming predictive factor for the requirement for manipulation.
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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 | 0.000 | 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".