Improving evidence on anticoagulant therapies for venous thromboembolism in children: key challenges and opportunities
Bibliographic record
Abstract
Venous thromboembolism (VTE) is increasingly diagnosed in pediatric patients, and anticoagulant use in this population has become common, despite the absence of US Food and Drug Administration (FDA) approval for this indication. Guidelines for the use of anticoagulants in pediatrics are largely extrapolated from large randomized controlled trials (RCTs) in adults, smaller dose-finding and observational studies in children, and expert opinion. The recently FDA-approved direct oral anticoagulants (DOACs), such as dabigatran, rivaroxaban, apixaban, and edoxaban, provide potential advantages over oral vitamin K antagonists and subcutaneous low-molecular-weight heparins (LMWHs). However, key questions arise regarding their potential off-label clinical application in pediatric thromboembolic disease. In this Perspective, we provide background on the use of LMWHs such as enoxaparin as the mainstay of treatment of pediatric provoked VTE; identify key questions and challenges with regard to DOAC trials and future DOAC therapy in pediatric VTE; and discuss applicable lessons learned from the recent pilot/feasibility phase of a large multicenter RCT of anticoagulant duration in pediatric VTE. The challenges and lessons learned present opportunities to improve evidence for anticoagulant therapies in pediatric VTE through future clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.136 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".