Assessing the Risk of Venous Thromboembolism in Patients with Hematological Cancers using Three Prediction Models
Bibliographic record
Abstract
Background: Assessment of individual VTE risk in cancer patients prior to chemotherapy is important. Risk assessment models (RAM) are available but have not been validated for hematological malignancy. We aimed to assess validity of the Vienna Cancer and Thrombosis Study (CATS) score in prediction of VTE in a variety of hematological 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 and patients were followed up for 6 months for VTE events. Khorana score (KS) was calculated. Plasma D-dimer and sP-selectin were measured then V-CATS score was calculated. We assessed modified V-CATS by using new cut off levels of d-dimer and sP-selectin based on ROC curve of the patients’ results. Results: Out of the 81 patients assessed, 2.7% had advanced cancer with metastasis. 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 were 87.5%, 87.5%, 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: Our data shows the usefulness of three VTE risk assessment models in hematological malignancies. Modified V-CATS score is more specific compared with V-CATS and KS, while all three scores have similar sensitivity. Implementation of RAM in hematological cancers can help improve the use of thromboprophylaxis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".