DO MALES REPORT A BETTER OUTCOME THAN FEMALES FOLLOWING HIGH TIBIAL OSTEOTOMY? A COHORT STUDY
Bibliographic record
Abstract
The primary purpose of this project was to examine the effect of gender on self reported outcome at least 2 years following high tibial osteotomy (HTO) for treatment of varus gonarthrosis. Complete data from 65 patients (24 female and 41 male) who previously underwent medial opening wedge HTO by surgeons at the Fowler Kennedy Sport Medicine Clinic (FKSMC) were available for analysis. Outcome Western Ontario and McMaster University Osteoarthritis Index (WOMAC) scores (dependent variable) were collected via telephone and the length of time from surgery was recorded. Pre operative and post-operative (2 years) mechanical axis angles (MAA) were measured from double leg standing hip-to-ankle radiographs. We used multiple linear regression to estimate the strength of the association between the post-operative WOMAC score and six independent predictor variables (gender, age, BMI, pre-op WOMAC, time (months), and MAA). Gender, age, BMI, time, pre-operative MAA (first model) and post-operative MAA (second model) were not significant contributors to WOMAC scores. Pre operative WOMAC scores approached significance in the pre-MAA model (β=0.26, 95% CI, -0.03 - 0.66, p=0.076) and were significant in the post-MAA model (β=0.36, 95% CI, 0.091 - 0.74, p=0.013). Residual analysis confirmed the data satisfied the four assumptions of multiple regression (normality, independence of error, linearity, homoscedasticity). The results of this study indicate that gender is not a significant predictor of outcome following 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".