Total knee replacement after high tibial osteotomy: time-to-event analysis and predictors
Bibliographic record
Abstract
BACKGROUND: An important aim of high tibial osteotomy (HTO) is to prevent or delay the need for total knee replacement (TKR). We sought to estimate the frequency and timing of conversion from HTO to TKR and the factors associated with it. METHODS: We prospectively evaluated patients with osteoarthritis (OA) of the knee who underwent medial opening wedge HTO from 2002 to 2014 and analyzed the cumulative incidence of TKR in July 2019. The presence or absence of TKR on the HTO limb was identified from the orthopedic surgery reports and knee radiographs contained in the electronic medical records for each patient at London Health Sciences Centre. We used cumulative incidence curves to evaluate the primary outcome of time to TKR. We used multivariable Cox proportional hazards analysis to assess potential preoperative predictors including radiographic disease severity, malalignment, correction size, pain, sex, age, body mass index (BMI) and year of surgery. RESULTS: Among 556 patients who underwent 643 HTO procedures, the cumulative incidence of TKR was 5% (95% confidence interval [CI] 3%–7%) at 5 years and 21% (95% CI 17%–26%) at 10 years. With the Cox proportional hazards multivariable model, the following preoperative factors were significantly associated with an increased rate of conversion: radiographic OA severity (adjusted hazard ratio [HR] 1.96, 95% CI 1.12–3.45), pain (adjusted HR 0.85, 95% CI 0.75–0.96)], female sex (adjusted HR 1.67, 95% CI 1.08–2.58), age (adjusted HR 1.50 per 10 yr, 95% CI 1.17–1.93) and BMI (adjusted HR 1.31 per 5 kng/m2, 95% CI 1.12–1.53). INTERPRETATION: We found that 79% of knees did not undergo TKR within 10 years after undergoing medial opening wedge HTO. The strongest predictor of conversion to TKR is greater radiographic disease at the time of 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".