Introduction to the 102nd Volume of the UTMJ Issue on The Anatomy of Uncertainty
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.104 | 0.042 |
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 source (direct Gemma or distilled Codex), 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".