{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T10:14:39Z","timestamp":1654596879283},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>Meta-analysis (MA) quantitatively summarizes the findings of independent studies and is considered the highest quality of evidence for evidence-based medicine. However, issues in reporting and methodological rigor of MA hamper reproducibility and create the potential for bias. By applying PRISMA reporting guideline and AMSTAR2 execution guidelines on 40 cervical cancer MA samples covering topics such as interventions and risk factors, we determined the extent to which MA execution adhered to best practice guidelines. The results show that the elements with least adherence include \u201creview methods established before MA\u201d and \u201cprincipal summary measures defined\u201d (each 32.5% per PRISMA) and \u201ccharacteristics of included studies\u201d (31.3% per AMSTAR2) which undermine reproducibility and increase the risk of bias. This initial work presents common pitfalls in MA and is intended to improve awareness of these issues for clinicians who are interested in conducting MA and to pave the way toward quality improvement via informatics approaches.<\/jats:p>","DOI":"10.3233\/shti220111","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:32:05Z","timestamp":1654594325000},"source":"Crossref","is-referenced-by-count":0,"title":["Quality Assessment of Meta-Analyses on Cervical Cancer"],"prefix":"10.3233","author":[{"given":"Sofia","family":"Milosavljevic","sequence":"first","affiliation":[{"name":"Harvard Medical School, USA"}]},{"given":"Pei-Yin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA"}]},{"given":"Hong","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA"}]},{"given":"Patricia Dolan","family":"Mullen","sequence":"additional","affiliation":[{"name":"School of Public Health, University of Texas Health Science Center at Houston, USA"}]},{"given":"Yang","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:32:06Z","timestamp":1654594326000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220111","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}