Controlled posterior condylar milling technique for unicompartmental knee arthroplasty minimises tibia resection during gap balancing: Short‐term clinical results
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to demonstrate the clinical utility of controlled posterior condylar milling (CPCM) in gap balancing while minimally resecting the tibia during fixed-bearing unicompartmental knee arthroplasty (UKA). METHODS: This study is a retrospective cohort study. Patients who underwent medial UKA for isolated medial compartment osteoarthritis with a minimum follow-up of 2 years were included. The patients were divided into two groups: the conventional group (n = 56) and the CPCM group (n = 66). In the CPCM group, the proximal tibia was resected at the level of the distal end of the subchondral bone. If the flexion gap was tighter than extension, the posterior condyle was additionally milled to adjust gap tightness. Standing knee X-ray and scanogram were used to evaluate alignment and tibia resection amount. Range of motion (ROM) and Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) scores were used to evaluate clinical outcomes. RESULTS: The CPCM group showed significantly smaller tibia resection (3.6 ± 1.9 mm) compared to the conventional group (5.2 ± 2.7 mm) (p < 0.001). Postoperative ROM (133.0 ± 8.3°, 135.2 ± 7.2°, n.s.) and WOMAC (19.3 ± 13.6, 23.6 ± 17.7, n.s.) were not significantly different between the two groups. Postoperative periprosthetic fractures occurred in two patients in conventional group, while the CPCM group had no periprosthetic fractures. CONCLUSION: The CPCM technique may be a simple and useful intraoperative technique that can achieve minimal tibia resection and promising clinical outcomes while easily adjusting gap tightness between flexion and extension during medial fixed-bearing UKA. LEVEL OF EVIDENCE: Level III.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".