Venous Thromboembolism Prevention During Asparaginase-Based Therapy for Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (vte) is a recognized complication in patients treated with asparaginase-containing chemotherapy regimens; the optimal preventive strategy is unclear. We assessed the safety and efficacy of prophylaxis using low-dose low molecular weight heparin in adult patients with acute lymphoblastic leukemia in complete remission treated with an asparaginase-based post-remission chemotherapy regimen. METHODS: As part of the intensification phase of the Dana-Farber Cancer Institute 91-01 regimen, asparaginase was administered weekly to 41 consecutive patients for 21-30 weeks; these patients also received prophylaxis with enoxaparin 40 mg daily (60 mg for patients ≥80 kg). Outcomes were assessed against outcomes in a comparable cohort of 99 patients who received the same chemotherapy regimen without anticoagulation prophylaxis. RESULTS: The overall rate of symptomatic venous thrombosis was not significantly different in the prophylaxis and non-prophylaxis cohorts (18.92% and 21.74% respectively). Among patients receiving prophylaxis, vte occurred in higher proportion in those who weighed at least 80 kg (42.86% vs. 4.35%, p = 0.0070). No major bleeding complications occurred in the prophylaxis group (minor bleeding: 8.1%). CONCLUSIONS: Prophylaxis with low-dose enoxaparin during the intensification phase was safe, but was not associated with a lower overall proportion of vte.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".