International guidelines regarding the role of IVIG in the management of Rh‐ and ABO‐mediated haemolytic disease of the newborn
Bibliographic record
Abstract
Haemolytic disease of the newborn (HDN) can be associated with significant morbidity. Prompt treatment with intensive phototherapy (PT) and exchange transfusions (ETs) can dramatically improve outcomes. ET is invasive and associated with risks. Intravenous immunoglobulin (IVIG) may be an alternative therapy to prevent use of ET. An international panel of experts was convened to develop evidence-based recommendations regarding the effectiveness and safety of IVIG to reduce the need for ETs, improve neurocognitive outcomes, reduce bilirubin level, reduce the frequency of red blood cell (RBC) transfusions and severity of anaemia, and/or reduce duration of hospitalization for neonates with Rh or ABO-mediated HDN. We used a systematic approach to search and review the literature and then develop recommendations from published data. These recommendations conclude that IVIG should not be routinely used to treat Rh or ABO antibody-mediated HDN. In situations where hyperbilirubinaemia is severe (and ET is imminent), or when ET is not readily available, the role of IVIG is unclear. High-quality studies are urgently needed to assess the optimal use of IVIG in patients with HDN.
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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".