{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T06:20:37Z","timestamp":1783405237196,"version":"3.54.6"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Large language models (LLMs) are increasingly considered for clinical documentation, referral support, and patient-facing communication. Biomedical accuracy alone may be insufficient for safe deployment if generated plans vary in access navigation, financial-access language, referral specificity, or tone across socially meaningful patient cues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate whether onomastic and bundled geographic-access signals are associated with differences in LLM-generated pediatric asthma referral plans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a cross-sectional 2\u2009\u00d7\u20092 factorial audit of seven commercial LLMs. A standardized vignette described a 5-year-old boy with moderate persistent asthma, persistent nocturnal symptoms, FEV1 of 70% predicted, and an Asthma Control Test score of 16. Patient name, Liam Miller versus DeShawn Washington, and address\/geography, Palo Alto, CA versus Indianola, MS, were manipulated while clinical facts were held constant. Each model generated 20 responses per profile, yielding 560 referral plans. Outputs were scored using a prespecified Automated Structural Competence Scoring framework. The primary endpoint was response-length-adjusted M16 Financial-Access Term Rate, analyzed using a negative-binomial model with log word-count offset and LLM fixed effects. Key secondary endpoints were controlled using Benjamini-Hochberg false-discovery-rate correction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the response-level primary model, the DeShawn name signal was associated with a higher financial-access term rate (IRR, 1.47; 95% CI, 1.23\u20131.77;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), as was the bundled geographic-access signal (IRR, 2.40; 95% CI, 2.02\u20132.85;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). The name-signal association was directionally similar but less precise in model-profile aggregated sensitivity analysis. The interaction term was below 1.0 (IRR, 0.79; 95% CI, 0.63\u20131.00;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.048), indicating no positive multiplicative synergy. Institutional Specificity and Triage Ranking were at ceiling. SDOH Recognition Depth, Location-Friction Acknowledgment, Navigator Recommendation, and Empathy\/Subjectivity differed by profile, whereas Access Priority remained low and non-significant after correction. Human validation showed moderate endpoint-specific reliability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>LLM-generated pediatric asthma referral plans varied in financial-access, geographic-access, navigation, SDOH-recognition, and selected tone-related framing. These findings do not establish discriminatory intent, clinical equivalence, downstream harm, or positive synergistic interaction, but support evaluating structural and access-related framing alongside biomedical content in clinical LLM audits.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1825576","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:45:50Z","timestamp":1783403150000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Profile-associated financial and access-related framing in LLM-generated pediatric asthma referral plans: a factorial audit of seven large language models"],"prefix":"10.3389","volume":"8","author":[{"given":"Zhendong","family":"Liu","sequence":"first","affiliation":[{"name":"Graduate School, Gansu University of Chinese Medicine","place":["Lanzhou, China"]},{"name":"Department of Pediatrics, Gansu Provincial Maternal and Child 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Graduate School, Gansu University of Chinese Medicine","place":["Lanzhou, China"]},{"name":"Department of Pediatrics, Gansu Provincial Maternal and Child Health Hospital","place":["Lanzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical 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