Canadian Rheumatology Association Meeting Virtual Conference
Bibliographic record
Abstract
The 76th Annual Meeting of the Canadian Rheumatology Association was held virtually on February 2–5, 2022. The program consisted of presentations covering original research, symposia, awards, and lectures. Highlights of the meeting include the following 2022 Award Winners: Distinguished Rheumatologist, John G. Hanly and Lori B. Tucker; Distinguished Teacher-Educator, Stephen Aaron; Emerging Investigator, Jessica Widdifield; Ian Watson Award for the Best Abstract on SLE Research by a Trainee, Maher Banjari; Phil Rosen Award for the Best Abstract on Clinical or Epidemiology Research by a Trainee, Molly Dushnicky; Best Abstract by a Rheumatology Resident, Wen Qi; Best Abstract on Basic Science Research by a Trainee, Omar Cruz Correa; Best Abstract by a Post-Graduate Research Trainee, Holly Philpott; Best Abstract on Quality Care Initiatives in Rheumatology, Michael Zeeman; Best Abstract by a Medical Student, Samir Magdy Iskander; Best Abstract by an Undergraduate Student, Daniel Onwuka; Best Abstract by a Rheumatology Post-Graduate Research Trainee, Jennifer Lee; Best Abstract on Research by Young Faculty, Nancy Maltez; Best Abstract on Pediatric Research by Young Faculty, Chelsea DeCoste; Best Abstract on Spondyloarthritis Research, Vanessa Ocampo; Practice Reflection Award, Gold, Bailey Dyck. Lectures and other events included: Keynote Lecture by Grace Wright: Towards Equity: Is Everyone in the Rheum Paving the Path to Equity with Diversity?; State of the Art Lecture by Tuhina Neogi: Pain Across the Spectrum of Rheumatic Diseases; Dunlop-Dottridge Lecture by Simon Carette: Vasculitis: What Have We Learned in the Past 50 Years?; and the Great Debate: Be it Resolved that the Rheumatology Healthcare Provider Is Responsible for Prescribing and Monitoring Physical Activity. Arguing for: Claire LeBlanc and Laura Passalent, and against: Arthur Bookman and Marie Clements-Baker. Topics including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, Sjögren syndrome, psoriatic arthritis, spondyloarthritis, vasculitis, osteoarthritis, fibromyalgia, and their respective diagnoses, treatments, and outcomes are reflected in the abstracts, which we are pleased to publish in this issue ofThe Journal of Rheumatology.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.350 | 0.100 |
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".