Intravenous Immunoglobulin for Repeated <scp>IVF</scp> Failure and Unexplained Infertility
Bibliographic record
Abstract
PROBLEM: We set out to determine whether intravenous immunoglobulin (IVIG) improves in vitro fertilization (IVF) success rates in women with a difficult history of multiple (≥ 2) prior IVF failures and /or 'unexplained' infertility. METHOD OF STUDY: A total of 229 women with multiple IVF failures (3.3 ± 2.1) and/or unexplained infertility (3.8 ± 2.7 years) were given IVIG on the day of egg retrieval, and the subsequent IVF success rates were compared with published success rates from the Canadian database (CARTR). RESULTS: The pregnancy rate per IVIG-treated cycle was 60.3% (138/229), and the live birth rate per IVIG-treated cycle was 40.2% (92/229). This is a significantly higher success rate compared to the Canadian average (30% live birth rate; CARTR statistics from 2010; P = 0.0012). In cases where a single embryo was transferred, pregnancy rate using IVIG was almost twofold the CARTR pregnancy rate [(61%(20/33) to 34.9% (428/1225)]. In cases where two high quality (≥ Grade 3) day 5 blastocysts were transferred, nearly a 100% pregnancy rate was achieved using IVIG (30/31). CONCLUSION: IVIG may be a useful treatment option for patients with previous IVF failure and/ or unexplained infertility. The data confirm previously published studies at other centers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".