Neonatal Aortic Thrombosis: A Comprehensive Review
Bibliographic record
Abstract
Background: Neonatal aortic thrombosis is a rare occurrence, but can be fatal. Treatment of this condition is hampered by the lack of large studies involving this pediatric population. Reporting of this condition is also not standardized. Methods: The purpose of this review is to collate available literature on the incidence, risk factors, presentation, treatment and outcome of neonatal aortic thrombosis as well as suggest a treatment model. Results: A Medline search of PubMed, OVID and Cochrane databases was undertaken using the key words “neonatal”, “infant”, “aorta”, “aortic”, “thrombosis”, “thrombus” and “clot”. Limits were set for articles that were English language only and published between 1980 and September 2009. Following review of all articles using predetermined search words and criteria, 38 were found with sufficient data for our purpose. The reported total number of neonatal patients with aortic thrombosis was 148 and 78% of the aortic thromboses in this review were related to arterial umbilical catheterization. Conclusions: We have suggested a classification system to standardize reporting of neonatal aortic thrombosis, as well as a treatment decision tree, and a clinical guide for the treatment of thrombosis in children. As always, clinicians should balance the risks and benefits of their decision to treat with the level of local expertise. This guide may specifically serve the neonatal population with line-related aortic thrombosis. Zusammenfassung Hintergrund: Neugeborenen-Aortenthrombosen sind seltenen, im klinischen Verlauf aber häufig tödlich. Die Behandlung dieser seltenen Erkrankung erfolgt individualisiert, da Studien fehlen. Methoden: Der Zweck dieses Überblickes ist es, vorhandene Literatur zu sichten, Risikofaktoren zu evaluieren und ein Behandlungsmodell vorzustellen. Ergebnisse: Eine Medline-Suche der Daten-banken PubMed, OVID und Cochrane wurde unter Verwendung der folgenden
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".