Assessing hypercoagulability and <scp>VTE</scp> risk using thromboelastography and Khorana score in women with cancers receiving chemotherapy
Bibliographic record
Abstract
Venous thromboembolism (VTE) is a common occurrence in cancer and chemotherapy increases thrombosis risk. Current risk assessment models such as the Khorana score (KS) and its modifications have limitations in female cancers. We assessed the coagulation profile of a group of women cancer patients under chemotherapy using thromboelastography (TEG) to determine if this can inform VTE risk assessment. Cancer patients who planned to receive chemotherapy were recruited. Baseline demographics, cancer data, BMI, Khorana Score (KS), and VTE risk factors were recorded and patients were followed for 6 months, for any thrombotic events. A total of 36 patients aged 35-85 (18 breast, 11 endometrial, 7 ovarian cancer) were evaluated. Hypercoagulability was detected in 63% of patients post-chemo cycle 1 and 75% post-cycle 2, with a significant increase in MA (maximum amplitude) and CI (clotting index), reduction in R (reaction time), K (clot kinetics), and LY30 (lysis time after 30 min of MA). KS showed only 7% of patients were high risk, 23% were low, and 70% were intermediate risk. MA and CI significantly increased in patients with intermediate and high-risk KS when compared with the low-risk patients and MA was positively correlated with KS. Five patients developed actual VTE; 100% of the tested ones were hypercoagulable either post-cycle 1 or 2 and 80% were KS intermediate risk. TEG is a hypercoagulability marker and TEG-MA and CI can potentially assess VTE risk. Larger studies are needed to assess the utility of TEG as an adjuvant to KS to better predict VTE in specific female cancers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".