Anti-Xa effect of a low molecular weight heparin (dalteparin) does not accumulate in extended duration therapy for venous thromboembolism in cancer patients
Bibliographic record
Abstract
Many patients with venous thromboembolism are being treated with low molecular weight heparin for extended periods of time. It is not certain if it is necessary to assess anti-Xa levels for extended treatment periods. This study is a prospective assessment of anti-Xa levels in patients on long-term therapy for acute venous thromboembolism who have active cancer. Consecutive consenting patients from one center in a multicenter trial that compared 6 months of low molecular weight heparin with oral anticoagulant therapy were treated with therapeutic doses of dalteparin (200 IU per kilogram) subcutaneously daily. Anti-Xa levels were assessed at the end of weeks 1 and 4,4-6 hours after injection of dalteparin. Patients were followed for bleeding and recurrent venous thromboembolism. There were 24 patients who had anti-Xa levels measured at weeks 1 and 4. Two other patients had week 1 measurements performed but died before the week 4 sample was collected due to their underlying cancer. The mean anti-Xa levels at weeks 1 and 4 were 1.11 and 1.03 anti-Xa units/ml respectively (P=0.13). These results suggest that for patients with active cancer receiving extended duration therapy with low molecular weight heparin (dalteparin) there is no accumulation of anti-Xa effect over the first month of therapy. Monitoring of anti-Xa levels in this situation is usually not required.
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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 | 0.001 |
| 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) | 0.001 | 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".