Antenatal Treatment with Intravenous Immunoglobulin to Prevent Gestational Alloimmune Liver Disease: Comparative Effectiveness of 14-Week versus 18-Week Initiation
Bibliographic record
Abstract
BACKGROUND: Antenatal therapy with high-dose intravenous immunoglobulin (IVIG) may prevent gestational alloimmune liver disease (GALD). OBJECTIVE: The objective of this study was to determine the effectiveness of this approach in a large cohort of women at risk for poor pregnancy outcome due to GALD. METHODS: Women with a history of affected offspring were provided antenatal IVIG treatment and data were acquired prospectively from 1997 to 2015. The outcomes of treated pregnancies were compared to those of untreated pregnancies, and the effectiveness of starting at 14 weeks was compared to that of starting at 18 weeks. RESULTS: A total of 188 treated pregnancies in 151 women were analyzed. Only 30% (n = 105) of untreated gestations resulted in healthy offspring as compared to 94% (n = 177) of treated pregnancies (p < 0.0001). Treated gestations of both the 14-week (n = 108) and the 18-week (n = 80) start cohort showed a decreased rate of fetal loss relative to untreated gestations (p < 0.0001). Equivalent outcomes were recorded in the 18-week versus the 14-week start cohort (p > 0.05). Few adverse events or complications of antenatal therapy were recorded. CONCLUSION: Antenatal therapy with high-dose IVIG initiated at either 18 or 14 gestational weeks effectively prevents poor outcome of pregnancies at risk for GALD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".