Enhancing prediction of thrombosis associated with breast cancer using prechemotherapy hematologic and coagulation characteristics
Bibliographic record
Abstract
INTRODUCTION: The applicability of venous thromboembolism (VTE) risk assessment models (RAMs), to breast cancer (BC) populations remains unclear. We aimed to compare the efficacy of current RAMs and examine the potential of additional hematologic parameters and thromboelastography (TEG); a point of care test, in improving VTE prediction in breast cancer (BC) patients. METHODS: In this pilot study, female BC patients were recruited before chemotherapy and followed for 6-12 months for VTE. VTE risk was assessed using Khorana score, Vienna CATS, PROTECHT, COMPASS-CAT, New Vienna CATSCORE, MDACC CAT, and hypercoagulability status. TEG and hematologic parameters were analyzed, and a modified RAM was developed. RESULTS: Among 47 patients, 5 (10.6%) developed VTE. PROTECHT was the strongest predictor [area under the curve (AUC) = 0.844], followed by Vienna CATS (AUC = 0.781). Adding immature granulocytes and red blood cell count to PROTECHT optimized prediction (AUC = 0.856). CONCLUSION: Incorporating hematologic parameters into PROTECHT may improve VTE risk prediction in BC patients, warranting further evaluation in larger studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".