Rare Disease Interest Group (rareDIG) at McGill University: A Medical Education Pilot Project
Bibliographic record
Abstract
One in 12 Canadians have a rare disease, yet medical education continues to espouse Dr. Woodward’s aphorism “when you hear hoofbeats think horses, not zebras.” This produces physician attitudes which are deleterious to the care of people with rare diseases. The McGill University Rare Disease Interest Group (rareDIG) has created programming which sensitizes medical students to the extent and reality of rare diseases.rareDIG helps them to develop attitudes and approaches which shorten the diagnostic odyssey and improve care of people with rare diseases. Success stems from drawing attention to the realities of rare disease through direct patient interaction, creating a strong social media presence, and building collaborations with rare disease advocacy groups and networks. Our inaugural Rare Disease Day event was attended by over 100 attendees including medical students, patients and their families, and a variety of health professionals.Other successes include a Patient Perspective Series addressing the holistic approach to rare disease, shadowing opportunities, “n = rare” journal clubs, and a “Humans of Rare Disease” advocacy project. Medical students represent an important cohort to target with rare disease awareness campaigns that has largely been overlooked by current advocacy efforts. By exposing medical students early in their education to the realities of rare diseases, student-run interest groups can improve medical students’ understanding and perception of rare diseases and ultimately improve patient care in the future. rareDIG strives to continue achieving its objectives in rare disease education and aide other medical schools in creating their own rare disease student groups.
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 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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".