Multivariable clinical-genetic risk model for predicting venous thromboembolic events in patients with cancer
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) is a leading cause of death among patients with cancer. Outpatients with cancer should be periodically assessed for VTE risk, for which the Khorana score is commonly recommended. However, it has been questioned whether this tool is sufficiently accurate at identifying patients who should receive thromboprophylaxis. The present work proposes a new index, TiC-Onco risk score to be calculated at the time of diagnosis of cancer, that examines patients' clinical and genetic risk factors for thrombosis. METHODS: We included 391 outpatients with a recent diagnosis of cancer and candidates for systemic outpatient chemotherapy. All were treated according to standard guidelines. The study population was monitored for 6 months, and VTEs were recorded. The Khorana and the TiC-Onco scores were calculated for each patient and their VTE predictive accuracy VTEs was compared. RESULTS: We recorded 71 VTEs. The TiC-Onco risk score was significantly better at predicting VTE than the Khorana score (AUC 0.73 vs. 0.58, sensitivity 49 vs. 22%, specificity 81 vs. 82%, PPV 37 vs. 22%, and NPV 88 vs. 82%). CONCLUSIONS: TiC-Onco risk score performed significantly better than Khorana score at identifying cancer patients at high risk of VTE who would benefit from personalised thromboprophylaxis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".