Managing intra‐articular deformity in high Tibial osteotomy: a narrative review
Bibliographic record
Abstract
The joint line convergence angle (JLCA) has a normal range between 0° to 2°, which increases in magnitude depending on the severity and stage of osteoarthritis in the knee.The JLCA represents the interaction of the intra-articular deformity arising from the osteoarthritis and the surrounding soft tissue laxity. Therefore, the JLCA has become a vital parameter in analysing the long leg alignment views for corrective planning before osteotomy surgery. Recent studies have considered the influence on how the preoperative JLCA is measured and its influence on achieving accurate postoperative desired correction in high tibial osteotomy surgery.The JLCA also reflects the influence of soft tissue laxity in a lower limb malalignment and many surgeons encourage it to be taken into account to avoid non physiological correction and/or overcorrection with negatively impacted postoperative patient outcome.This present review addressed how to obtain an accurate preoperative measurement of the JLCA, its influence on postoperative deformity analysis and how to reduce errors arising from an elevated preoperative JLCA.We have proposed a formula to help determine the value to subtract from the planned correction in order to avoid an overcorrection when performing a corrective osteotomy.Level of clinical evidence IV, narrative review.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".