Introduction to the 102nd Volume of the UTMJ Issue on The Anatomy of Uncertainty
Notice bibliographique
Résumé
Volume 102 of the University of Toronto Medical Journal invites readers on a journey through one of medicine's fundamental yet challenging dimensions—uncertainty. Although scientific and technological advances continually expand our knowledge, medicine remains an art practiced under conditions of incomplete information. The articles in this edition illuminate various ways uncertainty manifests in clinical practice, diagnostics, anatomy, and healthcare technology, encouraging reflection on how medical professionals can embrace ambiguity to enhance patient care. The volume begins by highlighting clinical complexities with “Anesthetic Protocol for Patients with Hereditary Hemorrhagic Telangiectasia Undergoing Enteroscopy for Angiodysplastic Lesions,” showcasing how careful, tailored strategies help manage the unknowns of rare conditions. Similarly, “Darier-Ferrand Dermatofibrosarcoma Protuberans: A Rare Soft Tissue Tumor of the Breast and a Review of the Literature” presents the challenges of diagnosing and managing a rare breast tumour, emphasizing the delicate balance between evidence-based practice and individualized patient care when guidelines are scarce. “Looks like cancer but not: Maxillary Sinus Hemangioma, A Diagnostic Dilemma” explores diagnostic uncertainty through a benign lesion mimicking cancer, illustrating the critical role precise diagnostic strategies play in preventing overtreatment. "Bacteriology of Secondary Peritonitis and Relationship to Surgical Site Infection in a Tertiary Health Establishment in Southern Nigeria" further broadens the discussion to infectious disease, highlighting the hidden microbial factors involved in surgical-site infections – reminding us of the unseen but significant determinants of patient outcomes. Anatomical variability is vividly brought to life by “Deep Dissection of the Gluteal Region with Multiple Muscle and Nerve Variations and a Brief Literature Review,” revealing multiple unexpected muscle and nerve variations. These findings remind clinicians that the assumption of standard anatomy can be perilous, advocating for cautious and thorough surgical approaches. The study “Incidence of Dyslipidemia and Hyperglycemia Among Healthy Female Teachers in Nablus, Palestine” brings attention to uncertainty within apparently healthy populations, uncovering widespread undetected dyslipidemia and hyperglycemia. This research underscores the importance of vigilant preventive health practices even among groups presumed low-risk. Finally, “When Algorithms Meet Anesthesia: A New Era of Patient Safety” addresses the cutting-edge interface of artificial intelligence and anesthesia. This commentary navigates the potential and pitfalls UTMJ • Volume 102, Number 2, June 2025 of predictive algorithms, urging clinicians to thoughtfully integrate these powerful tools without sacrificing clinical judgment and ethical vigilance. Together, these articles emphasize that uncertainty is an inherent and valuable aspect of medicine. It invites humility, continuous learning, and innovation. As editors, we extend our gratitude to the authors, peer reviewers, and editorial team who brought these insights to life. To our readers, we offer this volume as both a resource and an inspiration – encouraging curiosity, resilience, and thoughtful engagement with the uncertain but exciting landscape of medicine. Welcome to Volume 102. May it strengthen your resolve to understand and skillfully navigate the anatomy of uncertainty 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,000 |
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 tête enseignante, 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 ».