Outcome Measures in Interventional Trials for Prevention or Treatment of Venous Thrombosis in the Pediatric Population
Bibliographic record
Abstract
Determining clinically relevant outcomes are one of the key issues in the design of clinical trials. Recently, a working group from the Perinatal and Pediatric Subcommittee of the Scientific and Standardization Committee of the International Society of Thrombosis and Haemostasis published a position article defining outcomes in interventional trials for prevention and treatment of deep vein thrombosis (DVT) in children. For these studies the primary efficacy outcomes are DVT and the primary safety outcomes are bleeding. The exact definitions for these outcomes are included in this review article. This article expands on these recommendations by summarizing and reviewing the current literature in pediatric anticoagulation studies; discussing the relationship of choice of outcomes to specific clinical trial design; providing a more detailed discussion on the rationale for the defined outcomes in pediatric anticoagulation trials, as well as a detailed examination of secondary and tertiary outcomes. With a growing interest in multicenter, multinational pediatric anticoagulation clinical trials, establishment of standardized outcome measures that are applied uniformly over all studies is critically important. Agreement from the international academic community on these outcomes is an important step in assuring well-designed clinical trials, which are implemented to optimize the prevention and treatment of DVT in children.
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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 | 0.082 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".