2160Performance of a machine learning model vs. IMPROVE score for VTE prediction in acute medically ill patients: insights from the APEX trial
Bibliographic record
Abstract
Background: Acutely ill hospitalized medical patients have an increased risk for venous thromboembolism (VTE). Current risk assessment models (RAM) have demonstrated modest performance, at best, in predicting the occurrence of VTE in these patients. Purpose: Evaluate the discrimination and calibration performance of a machine learning model in estimating VTE risk compared to the recommended IMPROVE score. Methods: The APEX trial was a multicenter, double-blind, placebo-controlled trial that randomized 7,513 hospitalized acutely ill medical patients to extended duration betrixaban vs. standard of care enoxaparin. Patients were followed for up to 77 days. A super learner algorithm was built to predict VTE through 77 days after hospitalization by combining generalized additive models (GAMs), LASSO and RIDGE regressions, Random Forests, and Gradient Boosted Machines. The IMPROVE score was calculated for each patient to serve as a comparator. C-statistics were used to evaluate discrimination and a boostrapped significance test was performed to calculate a p-value for the difference. The Hosmer–Lemeshow goodness-of-fit test and calibration curves assessed the reliability of the RAMs.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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".