Pith Number

pith:E7FSBVEF

pith:2026:E7FSBVEFZARIN5HMVEI66U3LWD

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Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics

Barbara Di Eugenio, Baris Karacan, Patrick Thornton

Supervised clinical section segmentation models drop in performance when moving from MIMIC-III to obstetrics notes, while zero-shot models remain robust after correcting for hallucinated headers.

arxiv:2602.17513 v2 · 2026-02-19 · cs.CL

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Claims

C1strongest claim

while supervised models perform strongly in-domain, their performance drops substantially out-of-domain. In contrast, zero-shot models demonstrate robust out-of-domain adaptability once hallucinated section headers are corrected.

C2weakest assumption

The new obstetrics dataset is representative of the broader domain and that manual correction of hallucinations provides a fair, scalable basis for comparing model performance.

C3one line summary

Supervised clinical section segmentation models perform strongly in-domain on MIMIC-III but degrade substantially out-of-domain on a new obstetrics dataset, whereas zero-shot LLMs show robust cross-domain performance after hallucination correction.

References

24 extracted · 24 resolved · 4 Pith anchors

[1] Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics 2021 · arXiv:2602.17513

[2] ONC serves as a realistic benchmark for studying section segmentation in underexplored clinical subdo- mains and is intended for community reuse

[3] Domain-Specific Evaluation of Supervised Models:We assess whether transformer- based supervised models originally trained on public datasets can effectively generalize to obstetrics notes. By comparin 2022

[4] Our exper- iments reveal challenges (e.g., hallucinated section headers) as well as the potential ben- efits of zero-shot strategies, especially when annotated data are scarce

[5] Related Work Before the emergence of advanced machine learn- ing and NLP techniques, early approaches to clin- ical section segmentation primarily relied on rule- based methods. Denny et al. (2008), f 2008

Cited by

2 papers in Pith

Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics

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

Receipt and verification

First computed	2026-06-02T01:03:43.870982Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

27cb20d485c82286f4eca911ef536bb0f0a6e4131f8ce8fecb5c5ca78973da14

Aliases

arxiv: 2602.17513 · arxiv_version: 2602.17513v2 · doi: 10.48550/arxiv.2602.17513 · pith_short_12: E7FSBVEFZARI · pith_short_16: E7FSBVEFZARIN5HM · pith_short_8: E7FSBVEF

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/E7FSBVEFZARIN5HMVEI66U3LWD \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 27cb20d485c82286f4eca911ef536bb0f0a6e4131f8ce8fecb5c5ca78973da14

Canonical record JSON

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    "abstract_canon_sha256": "172cfb676ceba420b1195d411313137bc6a886ad43f4a0c88b51c377df50f8dd",
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-02-19T16:25:07Z",
    "title_canon_sha256": "3a6c065996f56b3e4c50f5016062c371bcb30d5241a334dda3a4eafd87d9f725"
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