{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:23:17Z","timestamp":1772252597905,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,21]],"date-time":"2019-01-21T00:00:00Z","timestamp":1548028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>In an era where the volume of medical literature is increasing daily, researchers in the biomedical and clinical areas have joined efforts with language engineers to analyze the large amount of biomedical and molecular biology literature (such as PubMed), patient data, or health records. With such a huge amount of reports, evaluating their impact has long stopped being a trivial task. In this context, this paper intended to introduce a non-scientific factor that represents an important element in gaining acceptance of claims. We postulated that the confidence that an author has in expressing their work plays an important role in shaping the first impression that influences the reader\u2019s perception of the paper. The results discussed in this paper were based on a series of experiments that were ran using data from the open archives initiative (OAI) corpus, which provides interoperability standards to facilitate effective dissemination of the content. This method may be useful to the direct beneficiaries (i.e., authors, who are engaged in medical or academic research), but also, to the researchers in the fields of biomedical text mining (BioNLP) and NLP, etc.<\/jats:p>","DOI":"10.3390\/data4010018","type":"journal-article","created":{"date-parts":[[2019,1,22]],"date-time":"2019-01-22T03:08:22Z","timestamp":1548126502000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Identifying Author Confidence in Biomedical Articles"],"prefix":"10.3390","volume":"4","author":[{"given":"Mihaela","family":"Onofrei Pl\u0103mad\u0103","sequence":"first","affiliation":[{"name":"Institute of Computer Science, Romanian Academy-Iasi branch, 700481 Iasi, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9874-0642","authenticated-orcid":false,"given":"Diana","family":"Trandab\u0103\u021b","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, 700483 Ia\u0219i, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8116-053X","authenticated-orcid":false,"given":"Daniela","family":"G\u00eefu","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Romanian Academy-Iasi branch, 700481 Iasi, Romania"},{"name":"Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, 700483 Ia\u0219i, Romania"},{"name":"Cognos Business Consulting S.R.L., 7, Iuliu Maniu Blvd, 061072 Bucharest, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,21]]},"reference":[{"key":"ref_1","unstructured":"Cojocaru, S., Gaindric, C., and Drugu\u0219, I. 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