Use of low molecular mass heparin (enoxaparin) in newborn infants: a prospective cohort study of 62 patients
Bibliographic record
Abstract
OBJECTIVE: To detail low molecular mass heparin (enoxaparin) use in the first few months of life. DESIGN: Prospective, consecutive cohort of unselected newborn infants. METHODS: Newborn infants were divided into groups by gestational age, underlying condition, hepatic and renal function, thrombocytopenia, and prothrombin time (PT/INR). Groups were analysed with respect to many aspects of enoxaparin treatment using multivariate methods. RESULTS: Sixty two newborn infants received enoxaparin representing 5.39 treatment years. Thromboembolic events (TEs) occurred predominantly in the lower and upper venous system in the presence of indwelling catheters (69%). Preterm infants required longer than full term infants to achieve an anti-(factor Xa) level in the target range (six versus two days). Preterm infants required higher doses of enoxaparin than full term infants to maintain anti-(factor Xa) levels in the target range (2.1 v 1.7 mg/kg/12 h). Infants with congenital heart disease (CHD) required less enoxaparin than those without CHD to maintain an anti-(factor Xa) level in the target range (1.7 v 2.1 mg/kg/12 h). Impaired renal and liver function influenced the number of dose changes needed (three versus one a month). Complete or partial resolution of TE was accomplished in 59% of newborn infants. Four infants developed major bleeds (1.2% per patient year). Recurrent TE and clot extension occurred in three infants (0.9% per patient year). CONCLUSIONS: Preterm infants are more difficult to treat with enoxaparin than full term infants. Enoxaparin appears to be an alternative to treatment with standard heparin or no treatment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".