IVIg for recurrent implantation failure: the right treatment for the right patient?
Bibliographic record
Abstract
Abstract The effectiveness of intravenous immunoglobulin (IVIg) for patients with unexplained recurrent implantation failure (uRIF) remains debated. The objective of this study was to evaluate outcomes in patients with uRIF treated with intravenous immunoglobulin (IVIg) compared to a separate cohort of uRIF patients not receiving IVIg within our center. We performed a retrospective cohort study defining uRIF as \(\ge\) 3 unexplained previously failed high quality blastocyst transfer failures in patients with a body mass index < 35, aged < 42, non-smoking, with >7mm type I endometrium at time of transfers. Primary outcomes included live birth, miscarriage, or transfer failure. We documented IVIg side effects and maternal/fetal outcomes. Logistic regression analysis was used to assess for association of IVIg exposure with outcomes and adjust for confounders. The study included 143 patients, with a 2:1 ratio of controls to patients receiving IVIg treatment. The baseline characteristics were similar between groups. There was higher live birth rate (LBR) in patients receiving IVIg (32/49; 65.3%) compared to controls (32/94; 34%); p < 0.001). When stratifying patients into moderate and severe uRIF (respectively 3–4 and \(\ge\) 5 previous good quality blastocyst transfer failures), only patients with severe uRIF benefited from IVIg (LBR (20/29 (69%) versus 5/25 (20%) for controls, p = 0.0004). In the logistic regression analysis, IVIg was associated with a higher odds of live birth (OR 3.64; 95% CI: 1.78–7.67; p = 0.0004). There were no serious adverse events with IVIg. In conclusion, it is reasonable to consider IVIg in well selected patients with \(\ge\) 5 previous unexplained, high quality blastocyst transfer failures. A well-designed randomized controlled trial is needed to confirm these findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".