Intravenous immunoglobulin in isoimmune haemolytic disease of newborn: an updated systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIg) is used in neonates with isoimmune haemolytic disease to prevent exchange transfusion (ET). However, studies supporting IVIg had methodological issues. OBJECTIVE: To update the systematic review of efficacy and safety of IVIg in neonates with isoimmune haemolytic disease. METHODS: MEDLINE, Embase databases and Cochrane Central Register of Controlled Trials (Cochrane Library) were searched (from inception to May 2013) for randomised or quasi-randomised controlled trials comparing IVIg with placebo/controls in neonates with isoimmune haemolytic disease without any language restriction. Three investigators assessed methodological quality of included trials. Meta-analyses were performed using random effect model and risk ratio (RR)/risk difference (RD) and mean difference with 95% CI calculated. MAIN RESULTS: Twelve studies were included, ten trials (n=463) of Rh isoimmunisation and five trials (n=350) of ABO isoimmunisation (three studies had both population). Significant variations in risk of bias precluded an overall meta-analysis of Rh isoimmunisation. Studies with high risk of bias showed that IVIg reduced the rate of ET in Rh isoimmunisation (RR 0.23, 95% CI 0.13 to 0.40), whereas studies with low risk of bias that also used prophylactic phototherapy did not show statistically significant difference (RR 0.82, 95% CI 0.53 to 1.26). For ABO isoimmunisation, only studies with high risk of bias were available and meta-analysis revealed efficacy of IVIg in reducing ET (RR 0.31, 95% CI 0.18 to 0.55). CONCLUSIONS: Efficacy of IVIg is not conclusive in Rh haemolytic disease of newborn with studies with low risk of bias indicating no benefit and studies with high risk of bias suggesting benefit. Role of IVIg in ABO disease is not clear as studies that showed a benefit had high risk of bias.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".