A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Identifying patients unlikely to achieve meaningful improvement following total knee arthroplasty (TKA) supports more effective shared decision-making (SDM). This study aimed to develop and validate machine learning (ML) models that preoperatively predict insufficient clinical improvement one year after TKA using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) subscales and total scores, and to assess the important predictive variables. METHODS: A retrospective analysis was performed on consecutive primary TKA patients from 2004 to 2022 at a single tertiary hospital was conducted. Insufficient clinical improvement was defined as not achieving the minimal clinically important difference (MCID) for each WOMAC subscale and total. Candidate preoperative variables included demographics, comorbidities, knee range of motion, radiologic variables, and WOMAC scores. A variety of ML models were evaluated using performance metrics for calibration and discrimination, as well as decision curve analysis and Shapley additive explanations. RESULTS: Among the 3,810 TKAs included, the ExtraTrees model performed best for WOMAC pain, stiffness, function, and total scores, achieving AUCs of 0.92, 0.90, 0.87, and 0.89; recall rates of 0.79, 0.86, 0.70, and 0.83; and Brier scores of 0.09, 0.10, 0.11, and 0.06, respectively, along with demonstrating good calibration curves and net clinical benefit. Shapley additive explanations identified better preoperative WOMAC scores, osteoporosis, diabetes mellitus, older age, malignancy, and coronary artery disease as important predictors of insufficient clinical improvement. CONCLUSIONS: The ML models demonstrated good performance in preoperatively predicting insufficient clinical improvement at 1 year after TKA based on WOMAC. These models have the potential to enhance SDM and perioperative patient management by preoperatively identifying approximately 70% to over 80% of patients likely to experience insufficient clinical improvement, with a specificity of about 80%, and by providing explanations regarding associated factors.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".