Intravenous immunoglobulins improve live birth rate among women with underlying immune conditions and recurrent pregnancy loss: a systematic review and meta-analysis
Bibliographic record
Abstract
Intravenous immunoglobulin (IVIG) is increasingly used as a treatment for recurrent pregnancy loss (RPL) despite lack of clear evidence on efficacy. Recent data suggest IVIG might be more effective in a subgroup of women with an aberrant immunological profile. Therefore, a systematic review and meta-analysis of studies on the effectiveness of IVIG treatment on pregnancy outcome among women with RPL and underlying immunological conditions (e.g., elevated NK cell percentage, elevated Th1/Th2 ratio, diagnosis with autoimmune disorders) was conducted. Eight non-randomized controlled trials, including 478 women (intervention: 284; control: 194), met eligibility criteria. Meta-analysis showed that treatment with IVIG was associated with a two-fold increase in live birth rate (RR 1.98, 95% CI 1.44-2.73, P < 0.0001). The effect of IVIG was particularly marked in the subgroup of studies including patients based on presence of elevated (> 12%) NK-cell percentage (RR 2.32, 95% CI 1.77-3.02, P < 0.0001) and when starting intervention prior to or during cycle of conception (RR 4.47, 95% CI 1.53-13.05, P = 0.006). In conclusion, treatment with IVIG may improve live birth rate in women with RPL and underlying immune conditions. However, these results should be interpreted with caution as studies are limited by low number of participants and the non-randomized design, which represent seriously biases. Future randomized controlled trials in women with RPL and underlying immune conditions are needed before using IVIG in a clinical setting.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".