External Validation of Bleeding Risk and Recurrent Venous Thromboembolism (VTE) Risk Scores in patients with cancer associated-thrombosis.
Bibliographic record
Abstract
Introduction: Cancer-associated thrombosis (CAT) is a clinical challenge due to the higher risk of bleeding and recurrent VTE. The study aimed to externally validate, at 6 months, two bleeding risk scales (CAT-BLEED and B-CAT) and one recurrent VTE scale (Ottawa) in cancer patients undergoing anticoagulant treatment. Materials and methods: We included consecutive CAT patients from January 2008 to June 2022. The risk scales performance was assessed using the area under the receiver operating characteristic (ROC) curve, sensitivity (S), specificity, positive predictive value (PPV), and negative predictive value (NPV). Clinically relevant bleeding (CRB) was defined by ISTH criteria, and recurrent VTE was confirmed by imaging. Results: A total of 1,206 patients with CAT were included (52.6% male, mean age 63.9 years), of which 52.0% had metastasis. The most common cancers were colorectal (18.7%) and lung (16.4%). In the first 6 months, 65 CRBs were observed. The B-CAT scale (score ≥3) showed strong discrimination with an AUC of 0.74 (95% confidence interval [CI]: 0.68-0.80). Specificity was 74.3%, and NPV was 97.1%. The CAT-BLEED scale was validated (p=0.04), though the lack of a defined cut-off point limits its clinical use. During the first 6 months, 60 recurrent VTE cases occurred, with pulmonary embolism (PE) being the most common (46.7%). The Ottawa scale (1–3 points) showed low predictive capacity for recurrence risk (AUC 0.47; 95% CI: 0.44–0.50), with S of 45.8%, PPV of 5.9%, and NPV of 96.1%. Conclusion: We validated two scales predicting CRBs in the first 6 months after VTE. The B-CAT scale demonstrates high predictive ability for CRBs.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".