{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:49:14Z","timestamp":1765370954502,"version":"3.46.0"},"reference-count":13,"publisher":"S. Karger AG","issue":"1","license":[{"start":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:00:00Z","timestamp":1756080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:00:00Z","timestamp":1756080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomed Hub"],"abstract":"<jats:p>&lt;p&gt;Introduction: The internet is a major source of medical information for patients, yet the quality of online health content remains highly variable. Existing assessment tools are often labor-intensive, invalidated, or limited in scope. We developed and validated MedReadr, an in-browser, rule-based natural language processing (NLP) algorithm that automatically estimates the reliability of consumer health articles for patients and providers. Methods: Thirty-five consumer medical articles were independently assessed by two reviewers using validated manual scoring systems (QUEST and Sandvik). Interrater reliability was evaluated with Cohen\u2019s \u03ba, and metrics with \u03ba &gt; 0.6 were selected for model fitting. MedReadr extracted key features from article text and metadata using predefined NLP rules. A multivariable linear regression model was trained to predict manual reliability scores, with internal validation performed on an independent set of 20 articles. Results: High interrater reliability was achieved across all QUEST and most Sandvik domains (Cohen\u2019s \u03ba &gt; 0.6). The MedReadr model demonstrated strong performance, achieving R2 = 0.90 and RMSE = 0.05 on the development set and R2 = 0.83 and RMSE = 0.07 on the validation set. All model coefficients were statistically significant (p &lt; 0.05). Key predictive features included currency and reference scores, sentiment polarity, engagement content, and the frequency of provider contact, intervention endorsement, intervention mechanism, and intervention uncertainty phrases. Conclusion: MedReadr demonstrates that structural reliability scoring of online health articles can be automated using a transparent, rule-based NLP approach. Applied to English-language articles from mainstream search results on common medical conditions, the tool showed strong agreement with validated manual scoring systems. However, it has only been validated on a narrow scope of content and is not designed to analyze search results for specific questions or detect misinformation. Future research should assess its performance across a broader range of web content and evaluate whether its integration improves patient comprehension, digital health literacy, and clinician-patient communication. &lt;\/p&gt;<\/jats:p>","DOI":"10.1159\/000548163","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:10:10Z","timestamp":1765357810000},"page":"162-170","source":"Crossref","is-referenced-by-count":0,"title":["MedReadr: Development and Evaluation of an In-Browser, Rule-Based Natural Language Processing Algorithm to Estimate the Reliability of Consumer Health Articles"],"prefix":"10.1159","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7386-4323","authenticated-orcid":false,"given":"Joshua","family":"Winograd","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Autumn","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikit","family":"Venishetty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alia","family":"Codelia-Anjum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dean","family":"Elterman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naeem","family":"Bhojani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin C.","family":"Zorn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adithya","family":"Balasubramanian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Vickers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bilal","family":"Chughtai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"127","published-online":{"date-parts":[[2025,8,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1525-1497.2004.40153.x"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/nuf.12038"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1017\/S071498081100050X"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1997.03540390074039"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-018-0668-9"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.46814\/lajdv3n3-004"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.324.7337.573"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.11613\/bm.2012.031"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/5584_2019_396"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5999"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.2471\/BLT.21.287654"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1093\/her\/cyaf005"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3109\/17538157.2010.542529"}],"container-title":["Biomedicine Hub"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/karger.com\/article-pdf\/doi\/10.1159\/000548163","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:10:21Z","timestamp":1765357821000},"score":1,"resource":{"primary":{"URL":"https:\/\/karger.com\/article\/doi\/10.1159\/000548163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,25]]},"references-count":13,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1,1]]}},"URL":"https:\/\/doi.org\/10.1159\/000548163","archive":["Portico"],"relation":{"is-supplemented-by":[{"id-type":"doi","id":"10.6084\/m9.figshare.29978308","asserted-by":"subject"}]},"ISSN":["2296-6870"],"issn-type":[{"value":"2296-6870","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,25]]}}}