{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T20:46:54Z","timestamp":1764103614340,"version":"3.46.0"},"reference-count":83,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["530282197"],"award-info":[{"award-number":["530282197"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canadian Institutes of Health Research, and a Discovery","award":["RGPIN-2022-04811"],"award-info":[{"award-number":["RGPIN-2022-04811"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canadian Children Inflammatory Bowel Disease Network"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>In medical research and education, generative artificial intelligence\/machine learning (AI\/ML) models to synthesize artificial medical data can enable the sharing of high-quality data while preserving the privacy of patients. Given that such data is often high-dimensional, a relevant consideration is whether to synthesize the entire dataset when only a task-relevant subset is needed. This study evaluates how the number of variables in training impacts fidelity, utility, and privacy of the synthetic data (SD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>We used 12 cross-sectional medical datasets, defined a downstream task with corresponding core variables, and derived 6354 variants by adding adjunct variables to the core. SD was generated using 7 different generative models and evaluated for fidelity, downstream utility, and privacy. Mixed-effect models were used to assess the effect of adjunct variables on the respective evaluation metric, accounting for the medical dataset as a random component.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fidelity was unaffected by the number of adjunct variables in 5\/7 SDG models. Similarly, downstream utility remained stable in 6\/7 (predictive task) and 5\/7 (inferential task) SDG models. Where significant effects were observed, they were minimal, resulting, for example, in a 0.05 decrease in Area under the Receiver Operating Characteristic curve (AUROC) when adding 120 variables. Privacy was not impacted by the number of adjunct variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Our findings show that fidelity, utility, and privacy are preserved when generating a more comprehensive medical dataset than the task-relevant subset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings support a cost-effective, utility, and privacy-preserving way of implementing SDG into medical research and education.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf169","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T12:27:48Z","timestamp":1758716868000},"page":"1843-1854","source":"Crossref","is-referenced-by-count":1,"title":["Should we synthesize more than we need: impact of synthetic 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