Bringing medical education “home”: the university health network and St. Michael's hospital home dialysis fellowship
Bibliographic record
Abstract
PURPOSE OF REVIEW: Home dialysis remains variable and underused globally despite clinical and quality of life benefits for patients. Due to low patient volumes in many centers, it is challenging for nephrology trainees to gain adequate clinical exposure to achieve competency and comfort in patient management. In this review, we highlight the University of Toronto-affiliated University Health Network (UHN) and St. Michael's Hospital (SMH) Home Dialysis Fellowship as an educational model to improve competency and comfort in home dialysis. RECENT FINDINGS: The UHN/SMH Home Dialysis Fellowship offers diverse clinical exposure with over 300 patients on peritoneal dialysis and home hemodialysis. Trainees achieve competency through repetitive exposure to a large volume of patients. The fellowship leverages continuity and longitudinal follow-up across all stages of a patient's chronic kidney disease journey. The program fosters interprofessional collaboration to develop comprehensive patient management plans. The attending physicians are leading world experts and support the varied trainee academic and career goals through mentorship and sponsorship. SUMMARY: The UHN/SMH Home Dialysis Fellowship program combines evidence-based medical education principles with high volume, diverse, and complex patient populations to offer trainees an exceptional learning experience and the foundations to become the next generation of home dialysis experts.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".