Mid‐flexion laxity could be identified with continuous flexion‐arc gap assessment in patients with a large preoperative convergence angle
Bibliographic record
Abstract
PURPOSE: To analyze the incidence of intraoperative mid-flexion laxity using continuous flexion-arc gap assessment, risk factors for mid-flexion laxity, and clinical results in navigation-assisted total knee arthroplasty (TKA). METHODS: Ninety posterior-stabilized TKAs were performed under navigation guidance for patients with degenerative arthritis and varus deformity. Intraoperatively, the gap between the trial femoral component and insert was evaluated in the navigation system with continuous flexion-arc gap assessment. Each medial and lateral gap at flexion (90°) and extension (0°) were made to be less than 3 mm. Mid-flexion laxity was determined when the gap in the flexion range between 15° and 60° was 3 mm or more. The proportion of knees with mid-flexion laxity was investigated. The factors affecting mid-flexion laxity were identified in terms of demographics, preoperative convergence angle, and change in joint line height and posterior femoral offset. The Knee Society Score and Western Ontario and McMaster Universities Osteoarthritis Index were evaluated. RESULTS: There were 31 cases (34.4%) of lateral mid-flexion laxity (average peak mid-flexion gap = 3.7 mm). The other 59 cases did not show mid-flexion laxity. The preoperative convergence angle was the only significant factor affecting lateral mid-flexion laxity (odds ratio = 1.466, p = 0.002). There were no significant differences in the clinical results between the groups with and without mid-flexion laxity. CONCLUSIONS: The continuous flexion-arc gap assessment was useful in evaluating mid-flexion laxity using navigation-assisted TKA. The preoperative convergence angle, reflecting soft-tissue laxity, can be a practical and simple radiographic finding for predicting lateral mid-flexion laxity. LEVEL OF EVIDENCE: IV.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".