Canadian Rheumatology Association Meeting Fairmont The Queen Elizabeth Montreal, Quebec, Canada February 27 – March 2, 2019
Bibliographic record
Abstract
The 73rd Annual Meeting of The Canadian Rheumatology Association was held at the Fairmont The Queen Elizabeth, Montreal, Quebec, Canada February 27 – March 2, 2019. The program consisted of presentations covering original research, symposia, awards, and lectures. Highlights of the meeting include the following 2019 Award Winners: Distinguished Rheumatologist, Edward Keystone; Distinguished Investigator, Diane Lacaille; Teacher-Educator, Shirley Tse; Emerging Investigator, Glen Hazlewood; Best Abstract on SLE Research by a Trainee – Ian Watson Award, Alexandra Legge; Best Abstract on Clinical or Epidemiology Research by a Trainee – Phil Rosen Award, Lauren King; Best Abstract on Basic Science Research by a Trainee, Remy Pollock; Best Abstract for Research by an Undergraduate Student, Andrea Carboni-Jimènez; Best Abstract on Research by a Rheumatology Resident, May Choi; Best Abstract by a Medical Student, Leonardo Calderon; Best Abstract by a Post-Graduate Research Trainee, Carolina Munoz-Grajales; Best Abstract by a Rheumatology Post-Graduate Research Trainee, Andre Luquini; Best Abstract on Quality Care Initiatives in Rheumatology, Cheryl Barnabe and Ines Colmegna; Best Abstract on Research by Young Faculty, Bindee Kuriya; Practice Reflection Award, Gold, Jason Kur; Practice Reflection Award, Silver, May Choi. Lectures and other events included Keynote Lecture by Andre Picard: Quirky Past, Uncertain Future: The State of Medicare in Canada; Keynote Address by Diane Lacaille, Distinguished Investigator Awardee: Time to Re-Label Comorbidities in RA – Coexisting or Complications; State of the Art Lecture by Mark Roberts: Myositis and its Mimics; Dunlop-Dottridge Lecture by Gilles Boire: The 4-H of Biomarkers in Arthritis: A lot of Help, Potential Harm, Some Hype, Increasing Hope; and the Great Debate: Be it Resolved that Competency-based Medical Education will Result in Improved Quality of Care for Patients vs the “Old Way” of Training Rheumatologists. Arguing for: Mercedes Chan and Marie-Paule Morin, and against: Beth Hazel and Heather McDonald-Blumer. 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 .
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".