Evidence map›Paper›PMID 42211451›Full record

ArticleFrontiers in endocrinology2026

Phenotype-driven protocol switching is associated with improved ART outcomes under constant gonadotropin dosage: a self-controlled analysis of 4,632 cycles.

Jie Gao, Yiran Li

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Jie GaoShanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Yiran LiShanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Managing failure after an initial assisted reproductive technology (ART) cycle remains a significant clinical challenge. Clinicians often face a dilemma between repeating the index protocol, escalating gonadotropin (Gn) dosages, or switching to an alternative strategy. We aimed to evaluate whether phenotype-driven protocol switching, specifically addressing issues like over-suppression or poor embryo quality, is more effective than repeating the same protocol. Methods: This large-scale, retrospective, self-controlled study involved 4,632 patients who underwent two consecutive ART cycles between January 2010 and December 2025. Patients were stratified into four pathways based on their second-cycle strategy: (i) Releasing Suppression (Long-to-Antagonist, n=1,412); (ii) Boosting Quantity (Mild-to-Antagonist, n=220); (iii) Improving Quality (Antagonist-to-Mild, n=829); and (iv) Reference (Antagonist-to-Antagonist, n=2,171). The primary outcome was the cumulative live birth rate (CLBR). Statistical analysis employed the Wilcoxon signed-rank test for paired continuous data and multivariable logistic regression to identify independent predictors of success. Results: In the Reference group, repeating the same protocol under identical Gn dosages significantly increased the CLBR (9.9% to 51.9%, P < 0.001). Beyond this baseline, individualised switching yielded distinct benefits. The Long-to-Antagonist switch significantly increased the median oocyte retrieval (8.0 [5.0-12.0] vs 9.0 [6.0-13.0], P < 0.001). The Mild-to-Antagonist switch successfully expanded the oocyte cohort in low responders (2.0 [1.0-4.0] vs 3.0 [2.0-5.2], P < 0.001). Conversely, the Antagonist-to-Mild switch did not increase oocyte yield (P = 0.642) but significantly improved the good embryo rate (median 0.0% vs 16.7%, P < 0.001). Multivariable regression confirmed that while age and anti-Müllerian hormone remained dominant prognostic factors, appropriate protocol triage neutralised risks without requiring dose escalation (P > 0.05 for protocol switch strategies). Conclusions: Phenotype-driven "quantity" or "quality" rescue is associated with significant clinical improvements following ART failure while keeping Gn dosages constant. While tactical switching is effective, a substantial proportion of the success in a second attempt is driven by natural biological variation (regression to the mean)."

Indexed as

GonadotropinsOvulation InductionReproductive Techniques, AssistedAdultFemaleHumansLive BirthPhenotypePregnancyPregnancy RateRetrospective StudiesGonadotropinsART failurecumulative live birth rateindividualized protocolovarian stimulationregression to the mean

Identifiers

PMID42211451
PMCPMC13212103

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.