University of Toronto: Journal Publishing Services
Not a member yet
21472 research outputs found
Sort by
Cognitive Profiles of People Living with Dementia: PCA and Clustering Analysis
No full textIntroduction: Dementia presents heterogeneous cognitive profiles that often transcend standard diagnoses. However, clinical classifications tend to overlook subtle differences in memory, language, executive, and visuospatial function. Data-driven subtyping may better capture this cognitive variability to inform personalized interventions. We aimed to identify, characterize, and evaluate the clinical relevance of distinct cognitive subtypes among patients with dementia using a dimensionality-reduction and clustering approach on TorCA data.
Methods: We analyzed cross-sectional data from 721 individuals (TDRA cohort) with dementia who completed a battery of neuropsychological tests covering memory, executive, language, and visuospatial domains. After data pre-processing (multicollinearity and missing values), we performed principal component analysis with multiple retention criteria to reduce dimensionality. Ward’s hierarchical clustering was applied on the selected principal components to derive cognitive subgroups. Cluster validity and profiles were assessed via silhouette scores and visualizations (biplots, radar charts).
Results: Four principal components were retained, reflecting overall severity and major cognitive domains. Hierarchical clustering based on these components revealed three robust cognitive subtypes: (1) predominant executive and visuospatial impairment; (2) broad language/fluency impairment; and (3) marked memory and visuospatial deficits. These subtypes cut across traditional diagnostic categories (e.g. Alzheimer’s, vascular dementia), underscoring heterogeneity within diagnoses. In an exploratory seven-cluster solution, clusters differed significantly in age, suggesting early-onset vs. late-onset cognitive profiles.
Discussion: A PCA-guided clustering approach identified clinically meaningful dementia subtypes that are not captured by conventional diagnoses. These data-driven subtypes highlight the importance of assessing multidomain cognitive patterns and offer potential for tailoring clinical interventions and improving prognostic accuracy
Comparing Post-Training Threshold Selection Methods for Predicting Pediatric Asthma-Related Readmissions
No full textBackground: In binary classification for healthcare applications, choosing an appropriate decision threshold is critical, as classification outcomes can directly affect patient care. In particular, clinical models for predicting emergency department (ED) or hospital readmissions must carefully balance the risk of false positives and false negatives.
Objectives: This project aims to compare four post-training threshold selection methods in the context of predicting asthma-related ED and hospital readmissions among pediatric patients, focusing on maximizing precision while maintaining a minimum recall level.
Methods: We evaluated Grid Search, GHOST, Bootstrap, and Order Statistics methods using both simulated data and real-world pediatric data from the Children’s Hospital of Eastern Ontario (CHEO). The goal was to identify thresholds that maximize precision while ensuring recall ≥ 0.80. Model performance was assessed based on precision, recall, and threshold stability across repeated runs.
Results: Grid and GHOST methods consistently achieved recall above 0.80, with Grid performing best. Order Statistics achieved the highest precision but often fell below the recall threshold. Bootstrap showed moderate performance in both precision and recall, with slightly less stability across repetitions.
Conclusion: When high recall is the priority, Grid and GHOST are preferred. Order Statistics is suitable for maximizing precision but may compromise recall. Bootstrap provides a reasonable balance. These findings support more informed threshold strategy decisions for clinical risk prediction models
Comparing Survival Outcomes and Secondary Outcomes of VATS versus Open Lobectomy for Early-Stage NSCLC: A Propensity Score Matched Analysis
No full textBackground: Video-assisted thoracoscopic surgery (VATS) is a minimally invasive technique for lobectomy in non-small-cell lung cancer (NSCLC), offering advantages such as reduced complications and quicker recovery compared to open lobectomy. However, the long-term survival benefits of VATS relative to open lobectomy remain unclear, particularly in observational studies where treatment selection may introduce bias.
Objectives: This retrospective study compared overall survival and secondary outcomes (e.g., length of hospital stay and complications) between VATS and open lobectomy among patients with early-stage NSCLC treated at Princess Margaret Hospital between 2002 and 2010. The study also attempted to address the gap in knowledge regarding the effectiveness of VATS when baseline characteristics differ between surgical methods.
Methods: Propensity score matching (PSM) was used to balance baseline covariates between the VATS and open lobectomy groups. Kaplan-Meier survival analysis and Cox proportional hazards models were applied to evaluate overall survival. Linear regression and logistic regression models were employed to analyze the length of hospital stay and the incidence of complications, respectively.
Results: After matching, there were no significant differences in overall survival between VATS and open lobectomy. VATS was associated with significantly shorter hospital stays (p-value ≤0.001) and a lower likelihood of complications (OR = 0.62, p-value = 0.05).
Conclusions: VATS and open lobectomy had comparable survival outcomes in patients with early-stage NSCLC after balancing baseline characteristics. VATS was associated with reduced hospital stay and complications, supporting its use as a viable alternative to open lobectomy in appropriate patients. These results suggest that VATS may offer clinical advantages, potentially influencing surgical decision-making in the management of NSCLC
Know Way Out: Epistemological Uncertainty and the Tacen in Junius 11
No full textIn Old English literature, the word tacen (sign) is a site of epistemological contestation. MS Junius 11 constructs a coherent theology of signs that offers a framework for navigating this contestation. Reading fourteen occurrences across the manuscript’s Old Testament poems, this essay argues that Junius 11 stages the repeated failure of visual and communal verification of the sign to demonstrate the limits of empirical and rational tests (false or ambiguous tacen, as with Eve’s vision), while demonstrating successful interpretations of the tacen through verification grounded in knowledge of God’s consistent character across time (e.g., Adam’s skepticism, the Israelites’ crossing at the Red Sea, and Abraham’s covenantal obedience). Situating these poems in the theological lineage of Augustine and the political context of the tenth-century Benedictine Reform, this essay proposes that Junius 11 models a theology of signs that anticipates and parallels the Benedictine Reform’s struggle over interpretative and institutional authority
A Models of School-Based Interventions: Protocols for Crisis Situations and Recovery Courses for the Promotion of Well-Being
No full textThis paper presents school-based intervention models designed to promote psychological well-being and manage crisis situations, with particular focus on suicide prevention. Two complementary approaches were developed: (1) well-being promotion courses based on the Five Ways to Wellbeing, and (2) Recovery Courses inspired by the Recovery College model, adapted to high school settings. Additionally, crisis management protocols were implemented to support schools following traumatic events such as student suicide.Both models emphasize co-design, active participation of students, teachers, and parents, and peer-to-peer support. In crisis contexts, interventions are individualized, phased, and coordinated with community mental health services. The results highlight how student involvement fosters emotional expression, resilience, and a sense of connectedness. These initiatives align with the Italian National Mental Health Plan 2025–2030, which promotes recovery-oriented, integrated pathways centered on autonomy, relationships, and social participation
Short-Term Forecasting of COVID-19 Positivity Rates in Ontario
No full textBackground & Objectives: Accurate forecasting of respiratory virus activity, particularly COVID-19, is vital for effective public health preparedness; yet many existing models fail to capture age-specific dynamics and temporal autocorrelation, limiting their predictive utility. This study aimed to improve short-term forecasting of COVID-19 percent positivity by integrating age-stratified modeling with time-series methods, addressing gaps in adaptability and predictive accuracy.
Methods: We developed age-specific Generalized Additive Models (GAMs) with lagged predictors and time-series components—including Random Walk (RW), Autoregressive (AR), and Moving Average (MA) terms—to capture non-linear trends in percent positivity while modeling both long- and short-term temporal dependencies. Models were stratified by age group and assessed under various outcome formulations: negative binomial models on case counts with offsets, logistic regression on aggregated counts, and beta regression on percent positivity. All models were fitted using R-INLA (v24.12.11). Rolling monthly time-series cross-validation was used for validation; Root Mean Squared Error (RMSE) guided model selection, while Mean Absolute Percentage Error (MAPE) assessed prediction accuracy.
Results: Age-stratified modeling significantly improved forecasts. For adults, autocorrelation terms enhanced short-term fluctuation capture, with informative priors reducing overfitting. In pediatric populations, seasonal effects related to school cycles were especially influential. Bayesian models outperformed standard methods during periods of trend shifts, particularly in winter months.
Conclusions: Integrating age-specific stratification, temporal components, and Bayesian inference improved COVID-19 forecasting accuracy. These approaches offer actionable insights for tailored public health interventions. Future work will extend this framework to influenza and RSV using multivariate time series models to understand interactions between respiratory viruses
Poésie et astronomie. De l’antiquité au romantism edited by Florian Barrière and Caroline Bertonèche
No full textAbstract: This collection of essays edited by Florian Barrière and Caroline Bertonèche presents an anthropological and cultural approach to astronomical themes in poetic works from antiquity to the Romantic period (with emphasis on Latin literature and British Romantic poetry). Apart from the main subject, the framework spans myth, theater, natural philosophy, and musical composition. Although the reader can tell that the emphasis is on the philological aspects of works, it is done with much careful consideration of contemporary scientific and philosophical debate and a good knowledge of the critical literature.Résumé : Ce recueil d’essais dirigé par Florian Barrière et Caroline Bertonèche propose une approche anthropologique et culturelle des thèmes astronomiques dans les œuvres poétiques de l’Antiquité à la période romantique (avec une attention particulière à la littérature latine et la poésie romantique britannique). Au-delà du sujet principal, le volume couvre également le mythe, le théâtre, la philosophie naturelle et la composition musicale. Bien que le lecteur perçoive que l’accent est mis sur les aspects philologiques des œuvres à l\u27étude, cette orientation s’accompagne d’une prise en compte des débats scientifiques et philosophiques contemporains ainsi que d’une connaissance approfondie des sources critiques
