Introduction to the 102st Volume of the UTMJ Issue on Technology in Medicine
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,187 | 0,060 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».