Immunology for Rheumatology Residents
Bibliographic record
Abstract
BACKGROUND: Immunologic mechanisms play an integral role in understanding the pathogenesis and management of rheumatic conditions. Currently, there is limited access to formal instruction in immunology for rheumatology trainees across Canada. AIM: The aims of this study were (1) to describe current immunology curricula among adult rheumatology training programs across Canada and (2) to compare the perceived learning needs of rheumatology trainees from the perspective of program directors and trainees to help develop a focused nationwide immunology curriculum. METHODS: Rheumatology trainees and program directors from adult rheumatology programs across Canada completed an online questionnaire and were asked to rank a comprehensive list of immunology topics. A modified Delphi approach was implemented to obtain consensus on immunology topics. RESULTS: Only 42% of program directors and 31% of trainees felt the current method of teaching immunology was effective. Results illustrate concordance between program directors and trainees for the highest-ranked immunology topics including innate immunity, adaptive immunity, and cells and tissues of the immune system. However, there was discordance among other topics, such as diagnostic laboratory immunology and therapeutics. CONCLUSIONS: There is a need to improve immunology teaching in rheumatology training programs. Results show high concordance between the basic immunology topics. This study provides the groundwork for development of future immunology curricula.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.263 | 0.080 |
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".