Use of intravenous immunoglobulin for treatment of recurrent miscarriage: a systematic review
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIG) is a fractionated blood product whose off-label use for treating a variety of conditions, including spontaneous recurrent miscarriage, has continued to grow in recent years. Its high costs and short supply necessitate improved guidance on its appropriate applications. OBJECTIVE: We conducted a systematic review of randomised controlled trials evaluating IVIG for treatment of spontaneous recurrent miscarriage. SEARCH STRATEGY: A systematic search strategy was applied to Medline (1966 to June 2005) and the Cochrane Register of Controlled Trials (June 2005). SELECTION CRITERIA: We included all randomised controlled trials comparing all dosages of IVIG to placebo or an active control. DATA COLLECTION AND ANALYSIS: Two investigators independently extracted data using a standardised data collection form. Measures of effect were derived for each trial independently, and studies were pooled based on clinical and methodologic appropriateness. MAIN RESULTS: We identified eight trials involving 442 women that evaluated IVIG therapy used to treat recurrent miscarriage. Overall, IVIG did not significantly increase the odds ratio (OR) of live birth when compared with placebo for treatment of recurrent miscarriage (OR 1.28, 95% CI 0.78-2.10). There was, however, a significant increase in live births following IVIG use in women with secondary recurrent miscarriage (OR 2.71, 95% CI 1.09-6.73), while those with primary miscarriage did not experience the same benefit (OR 0.66, 95% CI 0.35-1.26). AUTHOR'S CONCLUSIONS: IVIG increased the rates of live birth in secondary recurrent miscarriage, but there was insufficient evidence for its use in primary recurrent miscarriage.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".