Risk scores for occult cancer in patients with unprovoked venous thromboembolism: Results from an individual patient data meta‐analysis
Bibliographic record
Abstract
Background The Registro Informatizado de Pacientes con Enfermedad TromboEmbólica (RIETE) score and the Screening for Occult Malignancy in Patients with Idiopathic Venous Thromboembolism (SOME) risk scores aim to identify patients with acute unprovoked venous thromboembolism (VTE) at high risk of occult cancer, but their predictive performance is unclear. Methods The scores were evaluated in an individual patient data meta-analysis. Studies were eligible if enrolling consecutive adults with unprovoked VTE who underwent protocol-mandated screening for cancer. The primary outcome was a cancer diagnosis between 30 days and 2 years of follow-up. The discriminatory performance was evaluated by computing the area under the receiver (ROC) curve in random-effects meta-analyses. Results The RIETE score could be calculated in 1753 patients, of whom 63 (3.6%) were diagnosed with cancer. The pooled area under the ROC curve was 0.59 (95% confidence interval [CI], 0.52-0.66; I2 = 0%). Of the 427 patients (24%) classified as high risk, 25 (5.9%) were diagnosed with cancer compared with 38 of 1326 (2.9%) low-risk patients (hazard ratio [HR], 2.0; 95% CI, 1.3-3.4). The SOME score was calculated in 925 patients, of whom 37 (4.0%) were diagnosed with cancer. The pooled area under the ROC curve was 0.56 (95% CI, 0.46-0.65; I2 = 46%). Of the 161 patients (17%) classified as high risk (≥2 points), eight (5.0%) were diagnosed with cancer compared with 29 of 764 (3.8%) low-risk patients (HR, 1.2; 95% CI, 0.55-2.7). Conclusions The predictive discriminatory performance of both scores is poor. When used dichotomously, the RIETE score is able to discriminate between low- and high-risk patients. Because this is largely driven by advanced age, these results do not support the use of these scores in daily clinical practice.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".