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Key Points Question Are pediatric thyroid nodules amenable to cancer prediction by genomic classification? Findings In this retrospective study of 95 pediatric patients with thyroid nodules, surgical samples underwent next-generation sequencing and genomic classification. Testing defined the unique molecular landscape of pediatric thyroid nodules (which, as opposed to adults, comprised more frequent gene fusions and DICER1 variants) and identified a sensitivity of 96% and specificity of 78% regarding cancer detection. Meaning The study results suggest that although the molecular landscape of pediatric thyroid nodules is different than in adults, it remains amenable to multigene genomic classification, which may help prevent potentially unnecessary diagnostic surgeries.

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Objective Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, “dynamic” reaction picklist to improve allergy documentation in the electronic health record (EHR). Materials and Methods We split 3 decades of allergy entries in the EHR of a large Massachusetts healthcare system into development and validation datasets. We consolidated duplicate allergens and those with the same ingredients or allergen groups. We created a reaction value set via expert review of a previously developed value set and then applied natural language processing to reconcile reactions from structured and free-text entries. Three association rule-mining measures were used to develop a comprehensive reaction picklist dynamically ranked by allergen. The dynamic picklist was assessed using recall at top k suggested reactions, comparing performance to the static picklist. Results The modified reaction value set contained 490 reaction concepts. Among 4 234 327 allergy entries collected, 7463 unique consolidated allergens and 469 unique reactions were identified. Of the 3 dynamic reaction picklists developed, the 1 with the optimal ranking achieved recalls of 0.632, 0.763, and 0.822 at the top 5, 10, and 15, respectively, significantly outperforming the static reaction picklist ranked by reaction frequency. Conclusion The dynamic reaction picklist developed using EHR data and a statistical measure was superior to the static picklist and suggested proper reactions for allergy documentation. Further studies might evaluate the usability and impact on allergy documentation in the EHR.