Slight under-correction using individualized intentional varus femoral cutting leads to favorable outcomes in patients with lateral femoral bowing and varus knee
Bibliographic record
Abstract
PURPOSE: Restoration of neutral alignment is considered key in total knee arthroplasty (TKA). However, this may be undesirable and can result in medial soft-tissue over-release in patients with varus knee and lateral femoral bowing. This study aimed to determine whether individualized intentional varus distal femoral cutting yielded satisfactory clinical and radiological outcomes. METHODS: A total of 77 patients (91 knees) with varus knee (hip-knee-ankle axis ≥ 10°) and lateral femoral bowing > 5° underwent navigation-assisted primary TKA using individualized intentional varus distal femoral cutting. Knee Society scores, Western Ontario and McMaster Universities scores, and radiographs for limb alignment, implant alignment, and aseptic loosening were evaluated. Subgroup analyses were performed according to the limb alignment and coronal femoral component alignment (0° ± 3° vs. varus of > 3°). RESULTS: All clinical outcomes significantly improved at the final follow-up (p < 0.05 in all). The mechanical axis angle changed from 13.1° ± 2.7° to 2.8° ± 1.5°. The coronal femoral component angle at the final follow-up was 2.8° ± 1.3°. Radiolucent lines were observed in 6 cases (6.6%) and were less than 2 mm in all cases without progression. In subgroup analyses, no significant differences were observed in clinical outcomes (n.s. in all) and in the incidence of radiolucent lines (n.s. in limb alignment, n.s. in coronal femoral component alignment). CONCLUSIONS: Individualized intentional varus distal femoral cutting yielded favorable clinical outcomes without complications at 5-year follow-up. Slight under-correction using intentional varus distal femoral cutting could be a viable option in patients with varus knee and lateral femoral bowing during navigation-assisted TKA. 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 | 0.001 |
| 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) | 0.001 | 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".