The University of Pennsylvania study processed 400,000 Reddit posts from nearly 70,000 users spanning five years using large language models to map patient language onto MedDRA terms, surfacing menstrual irregularities in nearly 4% of posts and temperature dysregulation complaints alongside expected nausea signals. This methodology leverages real-world, unprompted reports at a scale clinical trials cannot match, yet the sample skews toward younger, English-speaking, digitally active demographics who may overrepresent certain experiences while under-sampling older or less vocal patients. Unlike the 2011 early social-media pharmacovigilance work co-author Lyle Ungar contributed to, today's LLMs enable faster standardization but still cannot establish causation, a limitation the authors correctly flag. Related analyses in JAMA Network Open (2024) on semaglutide adverse-event databases and a 2025 preprint in medRxiv examining FDA FAERS reports both independently noted menstrual changes, suggesting the Reddit pattern aligns with emerging pharmacovigilance signals rather than isolated online chatter. Mainstream coverage has focused on weight-loss efficacy while largely ignoring how post-market surveillance must now integrate unstructured patient narratives to keep pace with rapid drug adoption. Limitations include platform bias, lack of verified diagnoses, and potential amplification of rare anecdotes; the approach is therefore a hypothesis generator, not a replacement for randomized data.