An LLM and Retrieval Pipeline for Reproductive-Health Misinformation and Stigma

Background/Objectives: Reproductive-health topics such as polycystic ovary syndrome (PCOS), endometriosis, fertility, and menstruation attract both unsupported claims and stigmatizing framing on social media. We present an evidence-grounded pipeline combining large language models (LLMs) with biomedical retrieval, extended with a novel layer that scores the stigma framing of each claim independently of its truth. Methods: From 991 keyword-sampled English-language Bluesky posts we extracted 937 health claims, grounded each in evidence from PubMed, openFDA, and authoritative clinical guidelines (ACOG, NICE, NHS, WHO), and classified veracity into five categories using only the retrieved evidence. A parallel module scored five stigma dimensions and, separately, empowerment as a counter-stigma indicator. Results: Most claims (75.2%) were non-supported, though a relevance audit shows this largely records evidence that was not retrieved rather than claims shown to be false; the dominant frame was empowerment rather than overt shame. Stigma increased monotonically as claims departed from the evidence. The gradient was modest but robust (Kruskal–Wallis p < 10−7, ε2 = 0.039; ρ = 0.20), surviving cluster-aware reanalysis of claims nested within posts. Stigma varied sharply by condition, highest for infertility and endometriosis. Conclusions: Non-supported claims and stigmatizing framing co-occur, though this cross-sectional design cannot establish a direction. Single-coder validation was a pilot diagnostic check: stigma scores aligned directionally with human judgment, while five-way veracity agreement was limited. Results describe this keyword-sampled, single-platform corpus, not platform-wide prevalence.