Antithrombotic therapy and platelet transfusions in hematologic malignancy patients presenting chemotherapy‐induced thrombocytopenia: a French survey
Bibliographic record
Abstract
BACKGROUND: Patients with hematologic malignancies are at high risk for both thrombosis and bleeding. During the prolonged periods of thrombocytopenia experienced by patients who are receiving intensive chemotherapy, clinicians often hesitate to prescribe any protection against thrombosis. In case of anticoagulant prescription, it is the prescribers' responsibility to weigh risks and benefits for each patient. Current guidelines exist but do not take into account types of thrombosis, patients' comorbidities, or previous bleeding events. STUDY DESIGN AND METHODS: We proposed to gain insight into hematologists' beliefs about antithrombotic prescription in hematologic malignancy patients, to design future clinical trials. Therefore, we conducted a survey in France to evaluate the practices among a panel of hematologists. RESULTS: /L. None of the respondents decided to renounce treatment, nor to discontinue it because of thrombocytopenia, except in distal venous thrombosis or superficial vein thrombosis. One-fifth of clinicians proposed the insertion of a vena cava filter. CONCLUSION: As observed in the United States and Canada, we noticed discrepancies between recommendations and current practices in France. This highlights the urgent need to conduct studies to evaluate both efficacy and safety of antithrombotics in patients with hematologic cancer and thrombocytopenia.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".