Evidence map›Paper›PMID 42638723›Full record

ArticleFrontiers in public health2026

Quality, readability, and clinical-risk signals of default public-interface LLM responses to vascular and perioperative patient questions: a cross-sectional snapshot.

Wei Zhong, Yuanyuan Zhang, Yu Huang, Jin Yang, Guoxue Zheng, Qin Li, Gangzhi Li, Qin Liu

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wei Zhong *Department of Vascular Surgery, Suining Central Hospital, Suining, China.
Yuanyuan Zhang *Department of Anesthesiology, Suining Central Hospital, Suining, China.
Yu Huang *Department of Vascular Surgery, Suining Central Hospital, Suining, China.
Jin YangDepartment of Vascular Surgery, Suining Central Hospital, Suining, China.
Guoxue ZhengDepartment of Vascular Surgery, Suining Central Hospital, Suining, China.
Qin LiDepartment of Vascular Surgery, Suining Central Hospital, Suining, China.
Gangzhi LiDepartment of Vascular Surgery, Suining Central Hospital, Suining, China.
Qin LiuDepartment of Anesthesiology, Suining Central Hospital, Suining, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients increasingly use public large language model chatbot interfaces to seek health information. In vascular disease and perioperative management, default first responses may influence how patients interpret urgent symptoms, antithrombotic medications, procedural choices, and anesthesia-related safety issues. Methods: This cross-sectional benchmark study evaluated 110 default first responses returned by five publicly accessible LLM chatbot interfaces during a defined access window on May 28-29, 2026, Beijing time (UTC + 8). Interface names were recorded solely as the public-interface display labels visible at the time of access and should not be interpreted as independently verified API-level model identifiers. Each model was queried with 22 guideline-derived English patient-facing questions, yielding 110 responses. Responses were assessed using DISCERN, Ensuring Quality Information for Patients (EQIP), Global Quality Scale (GQS), Journal of the American Medical Association (JAMA) benchmark criteria, used only as a visible metadata/transparency proxy, six readability formulas, an investigator-developed Guideline Concordance Score, and an investigator-developed Potential Clinical-Risk Severity Flag. Differences across the evaluated public-interface response sets were tested using Friedman tests with Holm-adjusted Results: Interrater agreement was high for established instruments: DISCERN ICC(A,1) = 0.940, EQIP ICC(A,1) = 0.830, GQS weighted Conclusion: In this English-language benchmark of default first responses from five public LLM interfaces accessed through specific logged-in accounts from a Hong Kong, China IP address during a single late-May 2026 window, 107 of 110 responses received the maximum Guideline Concordance Score, and no response met the prespecified criteria for a high Potential Clinical-Risk Severity Flag. Pronounced ceiling and floor effects preclude conclusions about clinical sufficiency or safety. These findings should not be generalized to non-English use, different health-literacy levels, country-specific emergency-care pathways, other regions or account configurations, or later interface states.

Indexed as

ComprehensionLarge Language ModelsPerioperative CareVascular DiseasesCross-Sectional StudiesHumansclinical risklarge language modelspatient educationperioperative managementreadabilityvascular disease

Identifiers

PMID42638723
PMCPMC13500567

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.