Bibliographic record
Abstract
The incidence of thrombotic disorders in neonates and children is increasing with advances in diagnostic modalities, supportive care, and management of many health conditions. The developing coagulation system, need for intensive care, including catheterization, and co-morbid conditions are responsible for the relatively high risk of thrombosis in neonates compared to older children. This review addresses the advances over the last 3 years in neonatal thrombosis, with a focus on epidemiology, cerebral sinovenous thrombosis (CSVT), renal vein thrombosis (RVT), and portal vein thrombosis (PVT). The incidence of neonatal thrombosis in the contemporary era is reported to be 6.9-15 per 1,000 neonatal intensive care unit (NICU) admissions, compared to 2.4 per 1,000 NICU admissions reported in older registry data. The majority of recently published studies are small, retrospective, and from single centers, albeit they emphasize the need for definitive data to support the efficacy and safety of anticoagulation therapy (ACT) in the management of CSVT, RVT, and PVT. We highlight two important international initiatives geared towards improving the evidence base for these conditions. The International Pediatric Thrombosis Network (IPTN) is a collaboration of 74 centers across 27 countries (as of January 2021) which has started important projects like the international neonatal RVT registry, while the International Pediatric Stroke Study (IPSS) group is in the planning stages of a randomized controlled trial to evaluate the utility of ACT in the management of neonatal CSVT.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".