How to use unfractionated heparin to treat neonatal thrombosis in clinical practice
Bibliographic record
Abstract
Among children, neonates have the highest incidence of thrombosis due to risk factors such as catheter instrumentation, an evolving coagulation system and congenital heart disease. Unfractionated heparin (UFH) is one of the most commonly used anticoagulants in neonates. Published guidelines delineate dosing and monitoring protocols for UFH therapy in newborns. However, challenging clinical situations frequently present that warrants healthcare providers to think critically beyond the range of guidelines, and judiciously resolve specific problems. This review focuses briefly on the epidemiology of neonatal thrombosis and the use of UFH in this population. It is followed by a discussion on dosing of UFH in neonates, limited evidence that forms the basis of published guidelines with justification for a treatment regimen that precludes the use of a heparin loading dose in newborns and monitoring of UFH therapy with currently available tests such as antifactor Xa (anti-Xa) level and activated partial thromboplastin time (APTT). Multiple studies have demonstrated a lack of correlation between anti-Xa levels and APTT as well as between different anti-Xa assays. Many centers world-wide rely only on APTT for monitoring purposes and do not have access to anti-Xa assays. To address these difficulties, we propose two practical algorithms, with and without the use of anti-Xa levels that clinicians can follow when monitoring UFH therapy in neonates. The article concludes with an overview of the side-effects of UFH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".