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Record W4295755071 · doi:10.1111/bjh.18449

Intravenous immunoglobulin in the management of severe early onset red blood cell alloimmunisation

2022· article· en· W4295755071 on OpenAlexaff
Evangelia Vlachodimitropoulou, Tsz Kin Lo, Clarissa Bambao, Gregory A. Denomme, Gareth Seaward, Rory Windrim, Francine Tessier, Edmond Kelly, Tim Van Mieghem, Greg Ryan

Bibliographic record

VenueBritish Journal of Haematology · 2022
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicinePregnancyGestationFetusGestational ageObstetricsAntibodyBlood transfusionPediatricsImmunology

Abstract

fetched live from OpenAlex

Our objective was to assess the effect of maternal intravenous immunoglobulin (IVIG) administration for severe red blood cell (RBC) alloimmunisation on fetal outcomes. This is a case-control study. Women with a history of severe early onset alloimmunisation resulting in fetal loss in a previous pregnancy and high anti-D or anti-K antibody titres received IVIG in a subsequent pregnancy. We assessed gestational age at first transfusion and fetal outcomes in the subsequent pregnancy and compared these with the outcomes in the previous pregnancy. The most responsible antibody was anti-D in 17 women and anti-K in two others, whilst seven had more than one antibody. In all, 19 women received IVIG in 22 pregnancies, two of which did not even need an intrauterine transfusion (IUT). For previous early losses despite transfusion, IVIG was associated with a relative increase in fetal haemoglobin between treated and untreated pregnancies of 36.5 g/L (95% confidence interval 19.8-53.2, p = 0.0013) and improved perinatal survival (eight of eight vs. none of six, p = 0.001). For previous losses at <20 weeks, it enabled first transfusion deferral in subsequent pregnancies to at least 19.9 weeks (mean 23.2 weeks). Overall, IVIG decreases the severity of haemolytic disease of the fetus and newborn and allows deferral of the first IUT to a safer gestation in severe early-onset RBC alloimmunisation and rarely may even avoid the need for IUT entirely.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.216
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2022
Admission routes1
Has abstractyes

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