Canadian Rheumatology Association Annual Scientific Meeting; RBC Convention Centre (and Virtually); Winnipeg, Manitoba, Canada; February 28–March 2, 2024
Bibliographic record
Abstract
The 78th Annual Meeting of the Canadian Rheumatology Association was held at the RBC Convention Centre in Winnipeg, Manitoba, Canada, and virtually on February 28–March 2, 2024. The program consisted of presentations covering original research, symposia, awards, and lectures. Highlights of the meeting include the following 2024 Award Winners: Distinguished Rheumatologist, John M. Esdaile; Distinguished Investigator, Ann Clarke; Distinguished Teacher-Educator, Nicole Johnson; Emerging Investigator, Tom Appleton; Emerging Teacher-Educator, Pari Basharat; Ian Watson Award for the Best Abstract on SLE Research by a Trainee, Eugene Krustev; Phil Rosen Award for the Best Abstract on Clinical or Epidemiology Research by a Trainee, Derin Karacabeyli; Best Abstract on Research by a Rheumatology Resident, Shane Cameron; Best Abstract on Basic Science Research by a Trainee, Kaien Gu; Best Abstract by a Post-Graduate Research Trainee, Zoha Faheem; Best Abstract on Quality Care Initiatives in Rheumatology, Jean-Charles Mourot; Best Abstract by a Medical Student, Angel Gao; Best Abstract by an Undergraduate Student, Nicholas Chan; Best Abstract by a Rheumatology Post-Graduate Research Trainee, Carolina Munoz-Grajales; Best Abstract on Research by Young Faculty, Lauren King; Best Abstract on Pediatric Research by Young Faculty, Ruud Verstegen; Best Abstract on Spondyloarthritis Research, Fadi Kharouf. Lectures and other events included: Keynote Lecture by Jillian Horton: From Burnout to...What? Critical Frameworks for Tackling Our Burnout Crisis; State of the Art Lecture by Lihi Eder: From Psoriasis to Psoriatic Arthritis: Towards Prevention?; Dunlop-Dottridge Lecture by Hani El-Gabalawy: What is the Impact of RA on First Nations People, and How We Can Work with Communities to Detect and Prevent It?; and the Great Debate: Be it Resolved That EMRs Save Time for Healthcare Providers and Improve Quality of Care. Arguing for: Steven Katz and Jill Hall, and against: Tom Appleton and Dax Rumsey. 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 of The 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.299 | 0.111 |
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".