Partner Ethnicity and Assisted Reproductive Technology Outcomes: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Despite significant advances in assisted reproductive technology (ART), disparities in clinical outcomes persist. While patient-related factors are well-studied, the role of partner ethnicity remains understudied. We hypothesized that partner ethnicity affects ART outcomes. This study examined the association between partner ethnicity and ART outcomes. Methods: We conducted a retrospective cohort study among patients and their partners undergoing IVF treatment in the United Kingdom between 2017 and 2018. The exposure was partner ethnicity. Outcomes included biochemical pregnancy, clinical pregnancy, pregnancy loss, and live birth. We calculated risk ratios (RR) and 95% confidence intervals (CI) using multivariable regression models to estimate the association between partner ethnicity and IVF outcomes, adjusting for female patient age, partner age, patient ethnicity, gravidity, infertility diagnosis, treatment type, preimplantation genetic testing for aneuploidy, and number of prior in vitro fertilization (IVF) cycles. Results: Among 158,813 IVF cycles, live birth rates per cycle were 26.3% for couples with White partners and 23.1% for those with non-White partners. Non-White partners were associated with a 5% lower clinical pregnancy rate (RR 0.95, 95% CI 0.92–0.97) and a 6% lower live birth rate (RR 0.94, 95% CI 0.92–0.97). Specifically, Black (RR 0.82, 95% CI 0.77–0.87) and Asian (RR 0.67, 95% CI 0.59–0.76) partners had significantly reduced live birth rates, though these associations were attenuated after adjusting for patient ethnicity. Couples in which both the partner and patient were Black or Asian had 24–42% lower live birth rates compared with White couples (Black: RR 0.76, 95% CI 0.70–0.82; Asian: RR 0.58, 95% CI 0.49–0.68). Conclusions: Partner ethnicity is independently associated with IVF outcomes, with non-White partners showing lower rates of these outcomes. These findings suggest the clinical relevance of partner ethnicity in reproductive outcomes. Further research is warranted to elucidate the mechanisms underlying these associations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".