Spotlighting the Zebras: A Role for Medical Students in Shaping Rare Disease Care
Bibliographic record
Abstract
Rare diseases are collectively common, and thus very likely to be encountered in clinical practice. However, due in large part to deficits in medical training specific to these conditions, rare disease patients all-too often find themselves facing inadequate care. We are medical students representing institutions from the United States and Canada who believe that trainees can drive change in the landscape of rare disease care. In addition to highlighting a need for medical education to inculcate the knowledge and skills to effectively care for rare disease patients, we describe our efforts including a combination of peer-assisted learning, patient-oriented outreach, and interprofessional collaboration, which are intended to improve awareness of rare disease among future medical professionals.
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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 | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.004 | 0.048 |
| Research integrity | 0.014 | 0.043 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".