A lateral approach defect closure technique with deep fascia flap for valgus knee TKA
Bibliographic record
Abstract
BACKGROUND: Routinely, we use a midline skin incision and lateral parapatellar approach of the knee to perform valgus knee TKA (total knee arthroplasty). It is generally very difficult to close the lateral capsular defect after valgus knee TKA, especially for severe valgus and flexion knee deformity. METHODS: We describe a new surgical technique to close the lateral capsular defect with a deep fascia flap. From 2009 to 2012, we used the new technique to close lateral capsular defects for nine valgus TKA in eight patients. The wound healing, infection, range of motion, and postoperative X-ray Laurien view were evaluated. RESULTS: According to follow-up, we found that this technique can reduce the risk of intra- and postoperative complications (exposure of knee prosthesis, larger subcutaneous hematoma, poor wound healing, and higher risk of infection) and improve clinical outcome of total knee replacement (good range of motion and patellar tracking). There is no need for lateral parapatellar capsule Z-plasty during incision or filling the distal capsular defect with fat pad or composite meniscal-capsular-fat pad. CONCLUSION: Closing lateral capsular defect with a deep fascia flap for valgus knee TKA through a lateral parapatellar approach is a new and effective surgical technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".