Risk Scores for Occult Cancer in Patients with Venous Thromboembolism: A Post Hoc Analysis of the Hokusai-VTE Study
Bibliographic record
Abstract
Abstract Venous thromboembolism (VTE) may be the first sign of an undiagnosed cancer. In patients with unprovoked VTE, the risk is approximately 5% in the year following VTE diagnosis. Cancer-specific screening is therefore often considered in these patients, but the optimal screening strategy remains controversial. Recently, two risk classification scores have been proposed that may help in identifying patients at high risk of occult cancer in whom extensive screening may be warranted. In the present post hoc analysis of the Hokusai-VTE study, we evaluated the performance of the Registro Informatizado de Pacientes con Enfermedad TromboEmbólica (RIETE) and Screening for Occult Malignancy in Patients with Idiopathic Venous Thromboembolism (SOME) scores for occult cancer in patients with acute VTE. A total of 8,032 patients were included in the analysis of whom 218 (2.7%; 95% confidence interval [CI], 2.4–3.1) developed cancer between 30-day and 12-month follow-up. The c-statistics of the RIETE and SOME scores were 0.62 (95% CI, 0.57–0.66) and 0.59 (95% CI, 0.55–0.62), respectively. In patients classified as ‘high risk’, the cumulative incidence of cancer diagnosis during follow-up was 2.9% (95% CI, 2.1–3.9) for the RIETE score and 2.7% (95% CI, 1.9–3.7) for the SOME score, corresponding to hazard ratios of 1.8 (95% CI, 1.3–2.5) and 1.5 (95% CI, 1.04–2.2), respectively. In conclusion, the performance of both scores was poor. When used dichotomously, the scores were able to identify a group of patients with a significantly higher risk of occult cancer, although it remains unknown whether this translates into improved clinical important outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.001 | 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".