Assessing the risk of venous thromboembolism in patients with haematological cancers using three prediction models
Bibliographic record
Abstract
PURPOSE: Assessment of individual VTE risk in cancer patients prior to chemotherapy is critical for determining necessity of interventions. Risk assessment models (RAM) are available but have not been validated for haematological malignancy. We aimed to assess the validity of the Vienna Cancer and Thrombosis Study (V-CATS) score in prediction of VTE in a variety of haematological malignancies. METHODS: This is a prospective cohort study conducted on 81 newly diagnosed cancer patients undergoing chemotherapy. Demographic, clinical and cancer related data were collected, patients were followed up for 6 months, and VTE events were recorded. Khorana score (KS) was calculated. Plasma D-dimer and sP-selectin were measured, and then, V-CATS score was calculated. Receiver operator curve (ROC) was used to assess the sensitivity and specificity of RAMs. A modified V-CATS was generated and subsequently assessed by using new cut-off levels of d-dimer and sP-selectin based on ROC curve of the patients' results and compared the probability of VTE occurrence using all three RAMs. RESULTS: Among the 81 patients included in this study, a total of 2.7% were diagnosed with advanced metastatic cancer. The most frequent cancer was non-Hodgkin lymphoma (39.5%), and 8 patients (9.8%) developed VTE events. The calculated probability of VTE occurrence using KS, V-CATS and modified V-CATS scores at cut-off levels ≥ 3 was 87.5%, 87.5% and 100%, respectively. The AUC in ROC curve of modified Vienna CATS score showed significant difference when compared to that of V-CATS and KS (P = 0.047 and 0.029, respectively). CONCLUSION: The findings of our study highlight the value of three VTE risk assessment models in haematological malignancies. The modified V-CATS score demonstrated higher specificity compared to both V-CATS and KS, while all three scores exhibited similar sensitivity. We encourage the implementation of RAMs in haematological cancers for an appropriate use of thromboprophylaxis.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".