Early surgical outcomes and influencing factors of high tibial osteotomy
Bibliographic record
Abstract
Objective To investigate the influencing factors of functional recovery after high tibial osteotomy (HTO). Methods A retrospective research was carried on 98 patients who underwent HTO between January 2018 and December 2020. In each case, the medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), femoral tibial angle (FTA), hip-knee-ankle (HKA), weight bearing line (WBL) ratio of the knee joint, opening gap, opening angle, American knee society knee score (KSS), US Hospital for Special Surgery (HSS) score, Lysholm score, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were measured to determine postoperative function and influential factors of pain through logistic regression analysis. Results The follow-up time was between 18 and 42 months after operation with an average of 27.66 ± 12.9 per month. Overall functional scores were significantly improved. The influencing factors that may affect the postoperative effect of HTO include age and preoperative WBL ratio of the knee joint (WBL%). After incorporating these two factors into the multivariate logistic regression analysis, for every 1 unit increase in the preoperative WBL%, the probability of postoperative HSS being superior is 1.06 times higher than before [Exp(β): 1.062, 95% CI: 1.01–1.1, p = 0.018]. For every year increase in age, the probability of an excellent HSS score after surgery was 0.84 times higher than that before surgery [Exp(β): 0.843, 95% CI: 0.718–0.989, p = 0.036]. Preoperative WBL% ≥ 14.37 was 17.4 times more likely to be rated as excellent postoperative HSS than that <14.37 [Exp(β): 17.406, 95% CI: 1.621–186.927, p = 0.018]. Conclusion The postoperative functional scores of the patients significantly improved. Patients with preoperative WBL% ≥ 14.37% had better function after surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".