{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T12:01:13Z","timestamp":1790942473226,"version":"4.1.0"},"reference-count":20,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The American Surgeon\u2122"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The proliferation of Large Language Models necessitates evaluating their performance in communication accessibility. This study compares the linguistic architecture, readability, and automated psycholinguistic text properties of AI-generated breast cancer materials across varying clinical complexities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Twenty standardized questions across diagnosis and treatment categories were queried through three independent iterations. Readability was analyzed using Flesch-Kincaid Grade Level, SMOG, and Gunning Fog indices. Linguistic dimensions were evaluated via LIWC-22 software as computational style metrics. Statistical analyses utilized Wilcoxon, Mann-Whitney U, Spearman\u2019s correlation, and with False Discovery Rate (FDR) corrections. Cross-iteration stability was assessed with the intraclass correlation coefficient (ICC(2,1), two-way random-effects, and absolute agreement).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Gemini had better readability than ChatGPT (FKGL 9.23 vs 10.07, q = 0.022); both exceeded the 6th-grade threshold. Treatment queries increased linguistic complexity for both (ChatGPT FKGL q = 0.012; Gemini FKGL q = 0.047). Gemini scored higher on Analytic (q = 0.001) and Authentic (q = 0.021) dimensions; ChatGPT scored higher on I-words (q = 0.001) and Cognitive Processes (q &lt; 0.001). Treatment-specific shifts in Gemini Positive Tone (raw\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .041, q = 0.367) and ChatGPT Authentic (raw\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .025, q = 0.114) lost significance after FDR adjustment. ChatGPT showed a negative correlation between readability and Authentic score (\u03c1 = \u22120.63, q = 0.026). Cross-iteration ICC values were poor-to-moderate for both models (mean ICC: ChatGPT = 0.58, Gemini = 0.59).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Both LLMs create practical information gaps for patients with limited health literacy, particularly when addressing complex treatment pathways. Finer-grained psycholinguistic claims did not withstand statistical correction, and cross-iteration stability was modest. These automated analyses highlight structural limitations of current LLMs, reinforcing the necessity of clinician-guided curation before deploying them for patient education.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00031348261494138","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T08:18:21Z","timestamp":1790669901000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["When Treatment Gets More Complicated: AI Readability Declines in Breast Cancer Patient Education"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9859-5580","authenticated-orcid":false,"given":"Burak","family":"Altunpak","sequence":"first","affiliation":[{"name":"Istanbul Research and Training Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"e_1_3_7_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_3_7_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13187-020-01756-8"},{"key":"e_1_3_7_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2023.06.044"},{"key":"e_1_3_7_5_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2024.0836"},{"key":"e_1_3_7_6_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2023.1838"},{"key":"e_1_3_7_7_2","doi-asserted-by":"publisher","DOI":"10.2196\/52597"},{"key":"e_1_3_7_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s44271-024-00182-6"},{"key":"e_1_3_7_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/bmb\/ldaf017"},{"key":"e_1_3_7_10_2","unstructured":"ESMO. Breast Cancer: An ESMO guide for patients. Lugano Switzerland: European Society for Medical Oncology; 2018. Available from: https:\/\/www.esmo.org\/for-patients\/patient-guides\/patient-guides-by-language\/cancer-patient-guides-in-english."},{"key":"e_1_3_7_11_2","unstructured":"National Comprehensive Cancer Network. NCCN Guidelines for Patients: Invasive Breast Cancer. Plymouth Meeting PA: National Comprehensive Cancer Network; 2026. Available from: https:\/\/www.nccn.org\/patientresources\/patient-resources\/guidelines-for-patients\/guidelines-for-patients-details?patientGuidelineId=16"},{"key":"e_1_3_7_12_2","volume-title":"Manual for Clinicians Second Edition Help Patients Understand","author":"Weiss BD","year":"2007","unstructured":"Weiss BD. Manual for Clinicians Second Edition Help Patients Understand. AMA Foundation; 2007. Published online."},{"key":"e_1_3_7_13_2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.97525"},{"key":"e_1_3_7_14_2","doi-asserted-by":"publisher","DOI":"10.4274\/tod.galenos.2024.76743"},{"key":"e_1_3_7_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurjpc\/zwaf236.509"},{"key":"e_1_3_7_16_2","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000038352"},{"key":"e_1_3_7_17_2","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.230086"},{"key":"e_1_3_7_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/jpc.15336"},{"key":"e_1_3_7_19_2","doi-asserted-by":"publisher","DOI":"10.47941\/ijhs.1862"},{"key":"e_1_3_7_20_2","doi-asserted-by":"publisher","DOI":"10.1111\/jels.12413"},{"key":"e_1_3_7_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00700-y"}],"container-title":["The American Surgeon\u2122"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00031348261494138","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00031348261494138","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00031348261494138","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T11:48:07Z","timestamp":1790941687000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00031348261494138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":20,"alternative-id":["10.1177\/00031348261494138"],"URL":"https:\/\/doi.org\/10.1177\/00031348261494138","relation":{},"ISSN":["0003-1348","1555-9823"],"issn-type":[{"value":"0003-1348","type":"print"},{"value":"1555-9823","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,29]]},"article-number":"00031348261494138"}}