Computer-assisted total knee replacement in patients with arthritis and a recurvatum deformity
Bibliographic record
Abstract
We retrospectively reviewed the records of 1150 computer-assisted total knee replacements and analysed the clinical and radiological outcomes of 45 knees that had arthritis with a pre-operative recurvatum deformity. The mean pre-operative hyperextension deformity of 11° (6° to 15°), as measured by navigation at the start of the operation, improved to a mean flexion deformity of 3.1° (0° to 7°) post-operatively. A total of 41 knees (91%) were managed using inserts ≤ 12.5 mm thick, and none had mediolateral laxity > 2 mm from a mechanical axis of 0° at the end of the surgery. At a mean follow-up of 26.4 months (13 to 48) there was significant improvement in the mean Knee Society, Oxford knee and Western Ontario and McMaster Universities Osteoarthritis Index scores compared with the pre-operative values. The mean knee flexion improved from 105° (80° to 125°) pre-operatively to 131° (120° to 145°), and none of the limbs had recurrent recurvatum. These early results show that total knee replacement using computer navigation and an algorithmic approach for arthritic knees with a recurvatum deformity can give excellent radiological and functional outcomes without recurrent deformity.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".