Immunomodulation for unexplained recurrent implantation failure: where are we now?
Bibliographic record
Abstract
In brief: Immune dysfunction may contribute to or cause recurrent implantation failure. This article summarizes normal and pathologic immune responses at implantation and critically appraises currently used immunomodulatory therapies. Abstract: Recurrent implantation failure (RIF) may be defined as the absence of pregnancy despite the transfer of ≥3 good-quality blastocysts and is unexplained in up to 50% of cases. There are currently no effective treatments for patients with unexplained RIF. Since the maternal immune system is intricately involved in mediating endometrial receptivity and embryo implantation, both insufficient and excessive endometrial inflammatory responses during the window of implantation are proposed to lead to implantation failure. Recent strategies to improve conception rates in RIF patients have focused on modulating maternal immune responses at implantation, through either promoting or suppressing inflammation. Unfortunately, there are no validated, readily available diagnostic tests to confirm immune-mediated RIF. As such, immune therapies are often started empirically without robust evidence as to their efficacy. Like other chronic diseases, patient selection for immunomodulatory therapy is crucial, and personalized medicine for RIF patients is emerging. As the literature on the subject is heterogenous and rapidly evolving, we aim to summarize the potential efficacy, mechanisms of actions and side effects of select therapies for the practicing clinician.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".