{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:25:57Z","timestamp":1784903157936,"version":"3.55.0"},"reference-count":78,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Science and Technology Fund Project of the Health and Wellness Commission of Guizhou Province, China","award":["gzwkj2024-266"],"award-info":[{"award-number":["gzwkj2024-266"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Cancer, particularly tumors of the digestive system, presents a major global health challenge. The incidence and mortality rates of these cancers are increasing, and many patients face significant nutritional risks, which are often overlooked in clinical practice. This oversight can lead to serious health consequences, underscoring the need for effective nutritional assessment tools to improve clinical outcomes. Although several nutritional risk screening tools exist, their specific utility for patients with gastrointestinal tumors remains unclear. This study aimed to address this gap by systematically evaluating the performance of various nutritional screening tools in this patient population.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>A systematic search of six databases was conducted to identify studies that met predefined inclusion and exclusion criteria. Diagnostic test metrics such as sensitivity, specificity, and likelihood ratios (positive and negative) were estimated using a hierarchical summary receiver operating characteristic model. This approach was used to compare the accuracy of different nutritional screening scales.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>A total of 33 eligible studies were included in this meta-analysis, assessing six nutritional screening tools: the Malnutrition Universal Screening Tool, Malnutrition Screening Tool, Nutritional Risk Screening 2002, Mini Nutritional Assessment-Short Form, Nutritional Risk Index, and Patient-Generated Subjective Global Assessment. Among these, the Patient-Generated Subjective Global Assessment demonstrated the highest performance, with a sensitivity of 0.911 (95% confidence interval: 0.866\u20130.942) and a specificity of 0.805 (95% confidence interval: 0.674\u20130.891), outperforming the other screening tools.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>This study confirms the effectiveness of the Patient-Generated Subjective Global Assessment in identifying malnutrition risk among patients with digestive system tumors. However, as this research focused on a Chinese population, future studies should encompass a broader geographic scope and work toward standardized assessment criteria to enhance the global validation and refinement of nutritional screening tools.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0316070","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T18:34:17Z","timestamp":1734719657000},"page":"e0316070","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluating the accuracy of a nutritional screening tool for patients with digestive system tumors: A hierarchical Bayesian latent class meta-analysis"],"prefix":"10.1371","volume":"19","author":[{"given":"Menghao","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuexiu","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0114-6271","authenticated-orcid":true,"given":"Xiuqin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"issue":"1","key":"pone.0316070.ref001","first-page":"59","article-title":"Expert consensus on nutritional therapy for patients with malignant tumors","volume":"17","year":"2012","journal-title":"J Clin Oncol"},{"issue":"1","key":"pone.0316070.ref002","first-page":"451","article-title":"Global cancer statistics 2022: the trends projection analysis","volume":"10","author":"BS Chhikara","year":"2023","journal-title":"Chem Biol [lett.]."},{"issue":"3","key":"pone.0316070.ref003","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.","volume":"71","author":"H Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"issue":"5","key":"pone.0316070.ref004","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1097\/CM9.0000000000002108","article-title":"Cancer statistics in China and United States, 2022: profiles, trends, and determinants","volume":"135","author":"C Xia","year":"2022","journal-title":"Chin Med J (Engl)."},{"issue":"2","key":"pone.0316070.ref005","first-page":"1","article-title":"Interpretation of the 2020 global cancer statistics report","volume":"7","author":"Z Liu","year":"2021","journal-title":"Electron J Compr Cancer Ther"},{"key":"pone.0316070.ref006","volume-title":"Constructing a prognostic model for malignant tumor population based on GLIM criteria","author":"Y. 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