[Impact of lower limb weight bearing line ratio on motor function recovery after high tibial osteotomy].
Bibliographic record
Abstract
Objective: To determine the impact of the lower limb weight bearing line ratio (WBLR) on motor function recovery after high tibial osteotomy (HTO). Methods: A retrospective analysis was conducted on 55 patients with unilateral compartment knee osteoarthritis who underwent open-wedge HTO between August 2020 and October 2023 and met the selection criteria. Based on the postoperative Lysholm score, patients were divided into two groups: the good knee function group (Lysholm score≥90, group A) and the poor knee function group (Lysholm score<90, group B). Lysholm score, American Knee Society (AKS) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, and visual analogue scale (VAS) score for pain were compared between the two groups. Univariate analysis was performed on baseline data including gender, age, body mass index (BMI), affected side, disease duration, Kellgren-Lawrence grade, and radiographic parameters [preoperative and postoperative medial proximal tibial angle, lateral distal femoral angle, femoral-tibial angle, hip-knee-ankle angle (HKA), WBLR, posterior tibial slope angle, and joint line convergence angle] to identify factors influencing functional recovery. Multivariate logistic regression analysis was further used to identify independent factors. Additionally, receiver operating characteristic (ROC) curve analysis was employed to determine the optimal cut-off value of postoperative WBLR for predicting motor function recovery, and the area under curve (AUC) was calculated to assess diagnostic performance. Results: 0.001). Conclusion: A postoperative WBLR of 55.5% is associated with optimal motor function recovery after HTO.
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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 | 0.002 |
| 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) | 0.002 | 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".