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Context: The active surveillance (AS) program for papillary thyroid carcinoma (≤1 cm) at low risk (mPTC) showed a low percentage of progression., Objective: The aim of this study was to find a molecular signature of cases that showed disease progression during AS, which would allow their early identification., Methods: We performed next-generation sequencing of 95 fine-needle aspiration cytology specimens from cases prospectively enrolled in the AS program to analyze key somatic driver alterations or gene fusions implicated in PTC tumorigenesis. TERT promoter analysis was performed using Sanger sequencing or droplet digital polymerase chain reaction., Results: BRAF p.V600E was found in 66.3% (63/95) of mPTC and was the most common somatic alteration, followed by RAS oncogene mutations detected in 3.2% of mPTC (3/95: 2 NRAS and 1 KRAS) and gene fusions detected in 3.2% of mPTC (3/95: 1 RET-PTC1, 1 TFG-NTRK1, 1 ALK imbalance). No TERT promoter mutations (C228T and C250T) were found in the analyzed mPTC (84/95). The comparison between the molecular profile and the clinical outcome of the mPTC (stable vs progressive disease) showed no correlation (P = .6) and did not identify a molecular signature able to identify progressive mPTC., Conclusion: The molecular profile of mPTC is like that of bigger PTC with the exception that none of them showed a TERT promoter mutation. The identification of the most common driver mutations, such as BRAF, RAS, or gene fusions, is not helpful for the early identification of mPTC that will show disease progression during follow-up in the AS program., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. See the journal About page for additional terms.)

Questionnaire validation is indispensable in psychology and medicine and is essential for understanding differences across diverse populations in the measured construct. While traditional latent factor models have long dominated psychometric validation, recent advancements have introduced alternative methodologies, such as the "network framework." This study presents a pioneering approach integrating information theory, machine learning (ML), and Bayesian networks (BNs) into questionnaire validation. Our proposed framework considers psychological constructs as complex, causally interacting systems, bridging theories, and empirical hypotheses. We emphasize the crucial link between questionnaire items and theoretical frameworks, validated through the known-groups method for effective differentiation of clinical and nonclinical groups. Information theory measures such as Jensen-Shannon divergence distance and ML for item selection enhance discriminative power while contextually reducing respondent burden. BNs are employed to uncover conditional dependences between items, illuminating the intricate systems underlying psychological constructs. Through this integrated framework encompassing item selection, theory formulation, and construct validation stages, we empirically validate our method on two simulated data sets-one with dichotomous and the other with Likert-scale data-and a real data set. Our approach demonstrates effectiveness in standard questionnaire research and validation practices, providing insights into criterion validity, content validity, and construct validity of the instrument. (PsycInfo Database Record (c) 2025 APA, all rights reserved).