Use of intravenous immunoglobulin in neonates at a tertiary academic hospital: a retrospective 11‐year study
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIG) is used to treat a variety of diseases in the neonatal intensive care unit (NICU). Although audits have reported on the spectrum of IVIG use in adults, the indications and utilization in neonates has not been investigated. The objectives of this study were to describe the usage pattern of and indications for IVIG in a tertiary care NICU. STUDY DESIGN AND METHODS: A retrospective chart review was performed of all neonates who received IVIG in the NICU from January 2003 to December 2013. Data collected included patient demographic features, antenatal maternal details, neonatal laboratory results, treatment details, adverse events, and patient outcome. RESULTS: Thirty-seven neonates received IVIG over the 11-year period. Twenty-three (67%) were treated for hemolytic disease of the newborn (HDN); 13 treatments were ABO related, six were anti-D related, and four were for clinically significant antibodies. Fourteen (33%) were treated for non-HDN causes, including eight for septic neonates, two for neonates with necrotizing enterocolitis, two for neonates with a clinically significant antibody but without evidence of hemolysis, and two for neonates with glucose 6-phosphate dehydrogenase deficiency. A complete hemolytic workup was not performed consistently before the receipt of IVIG. CONCLUSIONS: This novel assessment of IVIG use in the NICU revealed the spectrum of disease for which IVIG is ordered. This study also found that key diagnostic tests needed to confirm an immune etiology for idiopathic jaundice are not performed routinely before IVIG receipt. Neonatal transfusion-related databases are needed to carry out pragmatic clinical trials to establish better evidence-based guidelines for IVIG therapy in the NICU.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".