Canadian Rheumatology Association Meeting Fairmont Empress Victoria, British Columbia, Canada February 26 – 29, 2020
Bibliographic record
Abstract
The 74th Annual Meeting of The Canadian Rheumatology Association was held at the Fairmont Empress, Victoria, British Columbia, Canada, February 26–29, 2020. The program consisted of presentations covering original research, symposia, awards, and lectures. Highlights of the meeting include the following 2020 Award Winners: Distinguished Rheumatologist, Jamie Henderson; Distinguished Investigator, Paul Fortin; Distinguished Teacher-Educator, Rayfel Schneider; Emerging Investigator, Claire Barber; Emerging Teacher-Educator, Dharini Mahendira; Ian Watson Award for the Best Abstract on SLE Research by a Trainee, Kimberley Yuen; Phil Rosen Award for the Best Abstract on Clinical or Epidemiology Research by a Trainee, Kristina Roche and Eugene Krustev; Best Abstract on Research by a Rheumatology Resident, Julie Mongeau; Best Abstract on Basic Science Research by a Trainee, Sonya Kim; Best Abstract by a Post-Graduate Research Trainee, Carolina Munoz-Grajales; Best Abstract on Quality Care Initiatives in Rheumatology, Arielle Mendel; Best Abstract by a Medical Student, Declan Webber; Best Abstract by an Undergraduate Student, Chloe Lee; Best Abstract by a Rheumatology Post-Graduate Research Trainee, Nancy Maltez; Best Abstract on Research by Young Faculty, Lily Lim; Best Abstract on Spondyloarthritis Research, Anas Samman; Practice Reflection Award, Gold, Steven Katz; Practice Reflection Award, Silver, Bailey Dyck. Lectures and other events included Keynote Lecture by Tim Spector: Inflammatory Diets — The Microbiome; Keynote Address by Paul Fortin, Distinguished Investigator Awardee: The Power of Many in Lupus Research; State of the Art Lecture by Dinesh Khanna: Systemic Sclerosis-related Lung Fibrosis: Management in 2020; Dunlop-Dottridge Lecture by Betty Diamond: Neuropsychiatric Lupus from Mechanisms to Treatment; and the Great Debate: To Diagnose or Not to Diagnose: Be It Resolved That It Is Better to Underdiagnose than Overdiagnose in Rheumatology Practice. Arguing for: Kam Shojania and Andrea Knight, and against: Amanda Steiman and Corrie Baldwin. 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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.426 | 0.154 |
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".