# Implicit Framing in Obstetric Counseling Notes: A Grounded LLM Pipeline on a VBAC-Eligible Cohort

arXiv: https://arxiv.org/abs/2604.23059 · HTML: https://mlsanity.com/paper/2604.23059/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [StructuredRAG: JSON Response Formatting with Large Language Models](https://mlsanity.com/paper/2408.11061/) (2024)
- [Spark NLP: Natural Language Understanding at Scale](https://mlsanity.com/paper/2101.10848/) (2021)
- [LLMs in Biomedicine: A study on clinical Named Entity Recognition](https://mlsanity.com/paper/2404.07376/) (2024)
- [Effects of Prompt Length on Domain-specific Tasks for Large Language Models](https://mlsanity.com/paper/2502.14255/) (2025)
- [Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models](https://mlsanity.com/paper/2403.02756/) (2024)
- [An Empirical Study on Prompt Compression for Large Language Models](https://mlsanity.com/paper/2505.00019/) (2025)
- [Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics](https://mlsanity.com/paper/2602.17513/) (2026)

## Abstract (arXiv)

Clinical framing -- the linguistic manner in which clinical information is presented -- can influence patient understanding and decision-making, with important implications for healthcare outcomes. Obstetrics is a high-stakes domain in which physicians counsel patients on delivery mode choices such as vaginal birth after cesarean (VBAC) and repeat cesarean section (RCS), yet counseling language remains underexplored in large-scale clinical text analysis. In this work, we analyze physician counseling language in 2,024 obstetric history and physical narratives for a rigorously defined cohort of patients for whom both VBAC and RCS were clinically viable options. To control for confounding due to medical contraindications, we first construct a VBAC-eligible cohort using structured clinical data supplemented by a large language model (LLM)-based extraction pipeline constrained to grounded, verbatim evidence from free-text narratives. We then apply a zero-shot LLM framework to categorize counseling segments into predefined framing categories capturing how physicians linguistically present delivery options. Our analysis reveals a significant difference in counseling framing distributions between VBAC and RCS notes; risk-focused language accounts for a substantially larger share of counseling segments in RCS documentation than in VBAC, with category-level differences confirmed by statistical testing, highlighting the value of controlled LLM-based framing analysis in obstetric care.