How to use low-molecular-weight 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. Low-molecular-weight heparins (LMWHs) are the most commonly used anticoagulants in neonates. Published guidelines delineate dosing and monitoring protocols for LMWH therapy in newborns. However, challenging clinical situations frequently present that warrant healthcare providers to think critically beyond the range of guidelines, and judiciously resolve specific problems. This review describes the use of LMWH in the neonatal population, including practical aspects such as route and site of administration, preparation from concentrated formulations and methods to minimize pain of subcutaneous injection. It is followed by a discussion on dosing, monitoring and outcomes of LMWH therapy in neonates. The risk of recurrence of thrombosis in neonates after LMWH therapy is approximately 3% based on a pooled analysis of studies reporting this outcome over the last 24 years. The article concludes with an overview of the side-effects of LMWH, including the risk of bleeding which is around 4% based on pooled analyses of more than 30 studies.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".