{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:28:23Z","timestamp":1761395303121,"version":"3.37.3"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2018,10,26]],"date-time":"2018-10-26T00:00:00Z","timestamp":1540512000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007928","name":"Perelman School of Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007928","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Globally, 36% of deaths among children can be attributed to environmental factors. However, no comprehensive list of environmental exposures exists. We seek to address this gap by developing a literature-mining algorithm to catalog prenatal environmental exposures.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We designed a framework called<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>PEPPER<\/jats:title>\n                  <jats:p>Prenatal Exposure PubMed ParsER to a) catalog prenatal exposures studied in the literature and b) identify study type. Using PubMed Central, PEPPER classifies article type (methodology, systematic review) and catalogs prenatal exposures. We coupled PEPPER with the FDA\u2019s food additive database to form a master set of exposures.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We found that of 31 764 prenatal exposure studies only 53.0% were methodology studies. PEPPER consists of 219 prenatal exposures, including a common set of 43 exposures. PEPPER captured prenatal exposures from 56.4% of methodology studies (9492\/16 832 studies). Two raters independently reviewed 50 randomly selected articles and annotated presence of exposures and study methodology type. Error rates for PEPPER\u2019s exposure assignment ranged from 0.56% to 1.30% depending on the rater. Evaluation of the study type assignment showed agreement ranging from 96% to 100% (kappa\u2009=\u20090.909, p\u2009&amp;lt;\u2009.001). Using a gold-standard set of relevant prenatal exposure studies, PEPPER achieved a recall of 94.4%.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Using curated exposures and food additives; PEPPER provides the first comprehensive list of 219 prenatal exposures studied in methodology papers. On average, 1.45 exposures were investigated per study. PEPPER successfully distinguished article type for all prenatal studies allowing literature gaps to be easily identified.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocy119","type":"journal-article","created":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T03:28:59Z","timestamp":1534217339000},"page":"1432-1443","source":"Crossref","is-referenced-by-count":5,"title":["Development and validation of the PEPPER framework (Prenatal Exposure PubMed ParsER) with applications to food additives"],"prefix":"10.1093","volume":"25","author":[{"given":"Mary Regina","family":"Boland","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Center for Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biomedical and Health Informatics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA, USA"}]},{"given":"Aditya","family":"Kashyap","sequence":"additional","affiliation":[{"name":"Data Science Masters Program, University of Pennsylvania, Philadelphia, PA, USA"}]},{"given":"Jiadi","family":"Xiong","sequence":"additional","affiliation":[{"name":"Data Science Masters Program, University of Pennsylvania, Philadelphia, PA, USA"}]},{"given":"John","family":"Holmes","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA"}]},{"given":"Scott","family":"Lorch","sequence":"additional","affiliation":[{"name":"Division of Neonatology, Department of Pediatrics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,10,26]]},"reference":[{"volume-title":"Preventing Disease through Healthy Environments. 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