Recommendations for standardized definitions, clinical assessment, and future research in pediatric clinically unsuspected venous thromboembolism: Communication from the ISTH SSC subcommittee on pediatric and neonatal thrombosis and hemostasis
Bibliographic record
Abstract
Clinically unsuspected venous thromboembolism (VTE) in children is defined as a VTE diagnosed via imaging test performed for surveillance (i.e., with an intent to identify clinically silent VTEs) or incidentally found (most often via imaging performed for evaluation of regional pathology unrelated to VTE) in the absence of any VTE-associated signs or symptoms. Our understanding of the clinical significance of these events in children is limited by a paucity of data on the epidemiology and outcomes of this complication. There is an urgent need for further research in this area to inform optimal management. Recognizing this knowledge gap, this Task Force has previously published a systematic review of the literature in this topic. We now provide guidance recommendations for standardization of definitions and identify future research needs on clinically unsuspected VTE in children. These recommendations will serve to enhance the quantity and quality of evidence on the topic and facilitate the design and execution of cooperative observational studies, and interventional trials of risk-stratified management approaches aimed at preventing and optimizing long-term outcomes of clinically unsuspected VTE 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 | 0.119 | 0.268 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".