Chagas disease remains a major public-health concern in endemic regions, and chronic cardiac involvement often manifests as conduction abnormalities detectable on standard 12-lead electrocardiograms (ECGs). Reliable automated screening remains challenging because of dataset heterogeneity and label uncertainty, particularly when combining strongly labeled cohorts with large weakly labeled repositories. Approach. We propose a hybrid architecture that integrates a 1D ResNet encoder for local ECG morphology, a bidirectional GRU for long-range temporal context, and handcrafted physiological features and demographics through an auxiliary-conditioned cross-attention module. The auxiliary vector, comprising age, sex, and QRS/conduction descriptors, is projected into a query token that selectively attends to deep sequential embeddings for feature-aware temporal aggregation. To exploit heterogeneous sources while reflecting source reliability, we further adopt a source-aware weighted binary cross-entropy objective. Main results. As team CAUETUMN in the PhysioNet/Computing in Cardiology Challenge 2025, the framework achieved a score of 0.347 on the organizer-held REDS-II leaderboard-validation set during the official phase and 0.218 on the final hidden test set, ranking 17th among 41 eligible teams. Significance. These results suggest that conditioning detection on interpretable QRS and conduction descriptors supports a transparent and physiologically informed screening framework, while highlighting the difficulty of generalizing across heterogeneous cohorts.

This paper introduces a nonparametric Bayesian latent class model tailored to longitudinal count data with an excess of zeros. By embedding zero-inflation mechanisms and allowing for an unbounded number of mixture components, the proposed approach effectively captures heterogeneous subpopulations while accounting for overdispersion. Specifically, an extended normalised gamma process prior links class membership probabilities to relevant predictors, enabling subjects with similar covariate profiles to form latent classes that capture distinct underlying patterns. In comprehensive simulations, the proposed model demonstrates consistently superior predictive performance and lower misclassification rates compared to competing Poisson, zero-inflated Poisson, and Dirichlet process mixture approaches, underscoring its flexibility and accuracy in modelling latent structures for count data. Empirical validation using longitudinal dental caries data from the Iowa Fluoride Study further confirms that the proposed model outperforms well-known competitors. These findings highlight the importance of integrating flexible mixture modelling with explicit zero-inflation components to address both structural zeros and inherent variability in heterogeneous populations.Co-authors: Yaeji Lim, Hang J. Kim, Beom Seuk Hwang

ContextThyroid cancer (TC) is a prevalent endocrine malignancy with rising incidence attributed to advancements in diagnostic technology. Despite its generally favorable prognosis, postsurgical complications, including hypoparathyroidism, can cause long-term health challenges.ObjectiveThis study evaluated the risk of nonskeletal complications in patients with TC with hypoparathyroidism (TC with hypoP).MethodsA retrospective cohort study was conducted using the National Health Insurance Service-National Sample Cohort (2002-2019), including patients with TC diagnosed between 2006 and 2019. Participants were categorized into TC with hypoP, TC without hypoparathyroidism (TC without hypoP), and matched controls. Propensity score matching and Cox proportional hazards models evaluated the incidence and risk of nonskeletal complications, including diabetes mellitus, dyslipidemia, cardiovascular and renal outcomes, and cataracts.ResultsThis study included 430 and 850 patients in the TC with hypoP and TC without hypoP groups, respectively, and their matched controls. The TC with hypoP group showed significantly higher risks of diabetes mellitus (HR 1.31, 95% CI 1.01-1.68), dyslipidemia (HR 1.29, 95% CI 1.06-1.57), urinary stones (HR 1.61, 95% CI 1.00-2.57), and cataracts (HR 1.50, 95% CI 1.15-1.95) than controls (all P < .05). Hypertension risk was higher in the TC with hypoP group vs the TC without hypoP group (HR 1.39, 95% CI 1.00-1.93, P = .048). Women had higher urinary stone risk, while cataract risk increased in patients aged over 50.ConclusionPatients with TC with hypoP are at an increased risk for specific nonskeletal complications, particularly older adults and women. These findings underscore the need for targeted monitoring and management strategies in this population. Further prospective studies are warranted to validate these associations and elucidate the underlying mechanisms.Co-authors: Eu Jeong Ku, Won Sang Yoo, Janghyeon Bae, Eun Kyung Lee, Hwa Young Ahn

In public health research, survival data denoting different causes of death are often collected across geographical regions. The data may cause invalid inference, however, if employed in a general competing risk model, which assumes constant relationships between risk factors and competing risks across regions. In addition, some applications might require spatially varying cause-specific hazard ratios. To address these limitations, this study proposes a geographically weighted cause-specific hazard regression (GWCHR) model to estimate spatially varying coefficients with a common spatial scale across multiple covariates. In identifying spatial variations of coefficients, we assign distance-based weights for each location in likelihood construction. We choose the bandwidth in the weighting function according to suitable selection criteria. We analyze the asymptotic properties of the proposed GWCHR model in detail. Our simulation studies compare the finite sample performance of the proposed model with general competing risk models. We apply the proposed method to prostate cancer data from Korea’s National Health Information Service database to examine the spatially varying effects of environmental and social factors on second primary cancers for prostate cancer patients.Co-authors: Mina Kim, Yeong-Hwa Kim, Molin Wang, Se Young Choi

BackgroundIncreasing evidence suggests that type 2 diabetes (T2DM) can influence the progression of Parkinson's disease (PD). However, it remains unclear whether T2DM increases the risk of progression to dementia and death in PD.ObjectiveThis study aimed to investigate the impact of T2DM on the risk of developing dementia and death following a diagnosis of PD.MethodsWe examined 158,962 individuals (aged 60 years or older) without PD or dementia using the Korean National Health Insurance Service (NHIS)-senior cohort database. A multi-state model was used to estimate the hazard ratios characterizing the effect of T2DM on the risk of PD, dementia, and death while adjusting for potential confounding factors. Results were analyzed according to age and sex.ResultsT2DM increased the risk of development of PD (adjusted hazard ratio [aHR]: 1.26, 95 % confidence interval [CI]: 1.11–1.42), dementia (aHR: 1.31, 95 % CI: 1.24–1.39), or death (aHR: 1.58, 95 % CI: 1.52–1.65) compared to those without T2DM. However, after PD diagnosis, T2DM was not associated with progression to dementia (aHR: 1.09, 95 % CI: 0.96–1.48) and death (aHR: 1.10, 95 % CI: 0.85–1.42) although subgroup analysis showed an elevated risk for the progression from PD to dementia (aHR: 1.41 95 % CI: 1.06–1.89) in individuals under 70 years of age.ConclusionsT2DM increases the risk of PD, dementia, and death in the elderly population. However, its effect on the progression to dementia and death may occur independently of the onset of PD, despite significant age-related heterogeneity.Co-authors: Seung Hyun Lee, Mina Kim, Da-woon Kim, Yun Su Hwang, Kye Won Park, Sungyang Jo, Ji-Hoon Kang, Richard J. Cook, Sun Ju Chung

Objective: Hypoparathyroidism is an uncommon but significant complication of total thyroidectomy. As the incidence of thyroid cancer increases, the prevalence of hypoparathyroidism also increases. However, its effect on the risk of osteoporotic fractures remains poorly understood. We aimed to evaluate the risk of osteoporotic fractures in patients with postoperative hypoparathyroidism compared with that in a matched control group. Design: This retrospective cohort study included 417 patients with hypoparathyroidism who had undergone total thyroidectomy for thyroid cancer (TC with hypoP) and 2085 matched controls from the Korean National Health Insurance Service-National Sample Cohort. Methods: The osteoporotic fracture rates and associated risk factors were analyzed in comparison after propensity score matching. Results: During a median follow-up period of 5.07 years (interquartile range 2.19-7.38), postoperative hypoparathyroidism was not associated with a significantly greater risk of osteoporotic fractures than that in the control group (hazard ratio [HR] 0.86, 95% confidence interval [CI], 0.59-1.24, P = .406). Age-specific analysis showed a trend toward higher fracture incidence in patients aged <50 years (HR 1.31, 95% CI, 0.53-3.22, P = .558) and a decreasing trend in those aged ≥50 years (HR 0.75, 95% CI, 0.51-1.12, P = .163), although the differences were not statistically significant. No significant differences in fracture risk were observed between the TC with and without hypoP groups across all fracture sites. The frequency of osteoporotic medication use was higher in the TC with hypoP group than in the control group, particularly among women aged <50 years. Conclusions: Hypoparathyroidism after total thyroidectomy does not appear to increase the risk of osteoporotic fractures compared with that in matched controls in clinical practice.Co-authors: Eu Jeong Ku, Won Sang Yoo, Yu Been Hwang, Subin Jang, Shinje Moon, Eun Kyung Lee, Hwa Young Ahn