Machine learning prediction of early reoperation following lower extremity tumor resection and endoprosthetic reconstruction: A PARITY trial secondary analysis
Bibliographic record
Abstract
BACKGROUND: Oncologic resection and endoprosthetic reconstruction of malignant bone tumors carries a high risk of complication and secondary surgery. Given the significant morbidity associated with reoperation in systemically compromised patients, accurate risk stratification is critical to patient counseling and shared decision-making. The purpose of this study was to develop a machine learning (ML) model for prediction of reoperation within one year of lower extremity tumor resection and endoprosthetic reconstruction. METHODS: Using data from the PARITY trial, 54 features across 604 lower extremity endoprosthetic reconstructions were evaluated as predictors of all-cause reoperation within one year. Logistic regression (LR), Random Forest, gradient boosting, AdaBoost, and XGBoost were used for model building. Standard metrics of area under receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and Brier scores were used to evaluate model performance. Important features for the top-performing model were determined. RESULTS: Of 604 lower extremity endoprosthetic reconstructions performed in the study period, 155 patients (25.7%) underwent at least one reoperation. The Gradient Boosting model had the highest discrimination (AUROC = 0.817, AUPRC = 0.690) of the tested models and was well-calibrated. Surgical site infection (SSI), operative time, white race, negative pressure wound therapy (NPWT) use, and female sex were the five most important features for model performance. CONCLUSIONS: We report a well-calibrated ML-driven algorithm with high discriminatory power for the prediction of all-cause early reoperation following lower extremity tumor resection and endoprosthetic reconstruction. Preventing SSI remains paramount to avoiding the morbidity of reoperation after complex oncologic limb salvage surgeries.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".