High tibial osteotomy: why we choose a lateral closing wedgetechnique and what is our “ideal” patient?
Bibliographic record
Abstract
Background: High tibial osteotomy (HTO) is indicated for medial compartment knee osteoarthritis in young, active patients to delay total knee replacement (TKR), which carries higher failure rates in this demographic. The procedure aims to redistribute mechanical loading from the degenerated medial compartment to the preserved lateral compartment. Objective: This article describes a specific lateral closing wedge HTO technique and evaluates preoperative predictors of long-term clinical success and survivorship. Key Points: The described technique utilizes a modified Coventry approach with a Krakow staple for stabilization, facilitating primary bone healing and early mobilization. In cases of medial collateral ligament pseudolaxity, a combined lateral closing and medial opening wedge modification is employed. A prospective 10-year study of 95 patients demonstrated an overall survivorship of 79% at 10 years. Three preoperative variables were significantly associated with superior outcomes: age under 55 years, body mass index (BMI) below 30 kg/m², and a Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score exceeding 45. Patients meeting these "ideal" criteria achieved a 97% survivorship rate at 10 years, compared to 69% for those with suboptimal preoperative profiles. Conclusion: Lateral closing wedge HTO provides durable functional improvement and high patient satisfaction. Precise patient selection based on age, BMI, and preoperative functional scores is essential to optimize long-term survivorship and clinical outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".