The effect of breastfeeding on treatment outcomes in in-vitro fertilization frozen embryo transfer cycles
Bibliographic record
Abstract
PURPOSE: While breastfeeding is known to impact unassisted conception, its effect on fertility treatment outcomes remains unclear. This study aimed to evaluate the effect of breastfeeding on in-vitro fertilization (IVF) frozen embryo transfer (FET) outcomes. METHODS: A retrospective cohort study at a single, urban, Canadian Fertility center, including women who underwent an FET(2015-2023). Breastfeeding women (study group) were compared to non-breastfeeding controls, matched 1:1 for age, treatment protocol, and year. We compared outcomes between the two groups. Primary outcomes were pregnancy rate (PR), clinical pregnancy rate (CPR),and live birth rate (LBR). Secondary outcomes included peak endometrial thickness prior to progesterone supplementation, biochemical PR and miscarriage rate. Multivariate logistic regression controlled for confounders. Two sub-analyses within the breastfeeding group examined treatment outcomes by breastfeeding duration (≤ 12 months; > 12 months) and prolactin levels (normal;elevated). RESULTS: Overall, 118 women met inclusion criteria (59 breastfeeding, 59 controls). Breastfeeding women, compared to controls,were characterized by a younger median age at initial oocyte collection (34.8 years(31.9-37.11) vs. 35.85 years(33.2-39.2),p = 0.044); higher median parity (1(1-1) vs. 0(0-1), p < 0.0001);and higher rates of irregular cycles (35.59% vs. 18.64%,p = 0.016);and elevated prolactin levels (16.95% vs. 1.69%,p = 0.003). Utilizing multivariate logistic regression adjusting for breastfeeding status,parity, age at initial oocyte collection, and cycle regularity, only maternal age at oocyte collection was associated with PR (adjusted odds ratio 0.84, 95% confidence interval 0.75-0.94,p = 0.003). Both sub-analyses revealed similar PRs, CPRs and LBRs between groups. CONCLUSIONS: Breastfeeding during an FET cycle was not associated with treatment outcomes, including PR,CPR, or LBR, providing cautious reassurance to physicians and patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".