Introduction to the 102st Volume of the UTMJ Issue on Technology in Medicine
Bibliographic record
Abstract
Volume 102 of the UTMJ arrives at a transformative moment in medicine, where rapid technological innovations are reshaping clinical practice, ethics, and healthcare delivery. As advancements in artificial intelligence, digital therapeutics, and remote monitoring redefine patient care, this issue explores the intricate interplay between technology and traditional medical practice, all while addressing the ethical, educational, and clinical challenges that arise. Our contributors offer a diverse collection of scholarly articles that not only probe critical clinical questions but also illuminate how technology is revolutionizing our understanding of health. We begin with “Cognitive Behavioural Therapy Outcomes for Clinical Perfectionism: A Scoping Review,” that examines the role of online and in-person CBT interventions to reduce the burdens of perfectionism. This study highlights how technology-driven approaches in modern medicine can be just as effective as traditional in-person methods while offering advantages in accessibility and scalability, addressing common barriers such as geographical constraints and limited resources. Complementing this perspective is “Moral Reasoning and Development in Medical School: A Literature Review,” which explores the education and evolution of ethical decision-making in contemporary medical learners. This review maps the trajectory of moral development in trainees, explaining that this decline has been linked to an educational approach that emphasizes compliance over critical engagement, a diminished focus on reflective practices, and a hidden curriculum that may conflict with formally taught ethical values. We highlight the need for robust ethics education that prepares future physicians to navigate the dilemmas posed by modern innovations in medicine, including AI and big data in healthcare. Volume 102 is further enriched by two compelling case reports, “A Bulky Primary Retroperitoneal Diffuse Large B-Cell Lymphoma: A Case Report” and “Atypical Chest Pain as a Prelude to Cancer: An Uncommon Presentation of Adenoid Cystic Carcinoma of the Parotid Gland.” This report emphasizes the challenges of diagnosing rare tumors but also highlights how modern medical technology, from enhanced imaging techniques to innovative diagnostic algorithms, plays a crucial role in discovering subtle signs of disease and informs management approaches. Bridging theoretical discussions of ethics and digital medicine with clinical practice, Volume 102 features two in-depth interviews that capture the spirit of technological innovation in medicine. We are honored to present an interview with Dr. Françoise Baylis, a luminary in bioethics whose work challenges and expands the ethical frameworks governing emerging technologies in gene editing and healthcare policy. Equally compelling is our conversation with Dr. Devin Singh, one of Canada’s pioneering physicians in clinical artificial intelligence. His insights as an emergency physician, educator, and entrepreneur provide a forward-looking perspective on harnessing AI to enhance clinical decision-making, address privacy concerns, and navigate the evolving regulatory landscape. Volume 102 of the UTMJ is more than a collection of academic articles; it is a reflection on the convergence of technology and medicine – a call to embrace innovation while rigorously examining its implications. We extend our deepest gratitude to the authors, reviewers, and editorial board for their intellectual contributions. To our readers, we hope this issue ignites curiosity, stimulates critical discourse, and inspires groundbreaking approaches that will shape the future of healthcare. Welcome to Volume 102 – a beacon for those committed to the relentless pursuit of knowledge, ethical practice, and technological excellence in medicine. Sincerely, David Chen and Alina Sami Editors-in-Chief University of Toronto Medical Journal
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".