Anticoagulant management of cancer-associated thrombosis and thrombocytopenia: a retrospective chart review
Bibliographic record
Abstract
Background: Patients with cancer-associated thrombosis (CAT) are at an increased risk of recurrent thrombosis and bleeding, especially if there is treatment- or disease-related thrombocytopenia. While direct oral anticoagulants (DOACs) are used in the management of CAT, low molecular weight heparin (LMWH) continues to be recommended for CAT with thrombocytopenia. Objectives: This study aimed to identify the rates of recurrent venous thromboembolism (VTE) and bleeding in patients with CAT and thrombocytopenia treated with DOACs compared with LMWH. Methods: A retrospective review of patients with CAT and thrombocytopenia (platelet count <100,000/μL) was conducted. Primary outcomes included rates of recurrent VTE and major bleeding over 90 days. Results: Forty-two patients met the inclusion criteria; 20 (47.6%) had a solid organ malignancy while 22 (52.4%) had a hematologic malignancy. Within the first 7 days of VTE, 3 (7.1%) patients had a platelet count <25,000/μL, 9 (21.4%) had 25,000 to 50,000/μL, and 19 (45.2%) had 50,000 to 100,000/μL. Sixteen patients (38.1%) received a DOAC for initial treatment, while 19 (45.2%) received LMWH. Among patients treated with DOACs, there were no recurrent VTEs, 2 clinically relevant nonmajor bleeding events (12.5%) within the first 2 weeks, and 1 minor bleed (6.3%) in the second month, while those treated with LMWH had 1 recurrent VTE (5.3%) in the second month and 2 clinically relevant nonmajor bleeding events (10.5%) within the first 2 months. Conclusion: Rates of thrombosis and major bleeding were similar among thrombocytopenic patients with CAT treated with DOACs and LMWH, although differences in baseline patient characteristics can be confounders. Further prospective research on the optimal anticoagulant management of CAT with thrombocytopenia is needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".