Canadian Rheumatology Association Meeting, February 4-7, 2015. Introduction, Abstracts, Author Index
Bibliographic record
Abstract
The 70th Annual Meeting of The Canadian Rheumatology Association (CRA) was held at the Fairmont Chateau Frontenac, Quebec City, Quebec, Canada, February 4–7, 2015. The program consisted of presentations covering original research, symposia, awards, and lectures. Highlights of the meeting include 2015 Award Winners: Distinguished Rheumatologist Award: Carter Thorne; Distinguished Investigator Award: Hani El-Gabalawy; Teacher-Educator Award: Andrew E. Thompson; Young Investigator Award: Sindhu Johnson; Summer Studentship Mentor Award: Lori Albert; Innovation in Education Award: Henry Averns; Best Abstract for Basic Science Research by a Trainee: Sina Rusta-Sallehy; Best Abstract for Research by an Undergraduate Student: Tristan Kerr; Best Abstract for Research by a Rheumatology Resident: Claire Barber; Best Poster by a Medical Student: Dennis Wong; Best Poster by a Post-Graduate Resident: Zainab Alabdurubalnabi; CRA/ARF Best Epidemiology/Health Services Research Award: Evelyn Vinet; CRA/ARF Best Clinical Research Award: Glen Hazelwood; CRA/ARF Best Basic Science Research Award: Carolina Landolt-Marticorena; Ian Watson Award for Best Abstract for SLE Research by a Trainee: Ripneet Puar; Phil Rosen Award for Best Abstract for Clinical or Epidemiology Research by a Trainee: Liam O’Neil. Lectures and more: The 2015 Dunlop Dottridge Lecture: HLA-B27, the Microbiome, and the Pathogenesis of Spondyloarthritis, by James Rosenbaum; Preventing Rheumatoid Arthritis: What We’ll Need to Know Before We Start Navigating this Uncharted Territory, by Distinguished Investigator Awardee Hani El-Gabalawy; and the 2015 State of the Art Lectures: A Genomic Approach to Understanding Rheumatic Disease, by Dr. Virginia Pascual, and Modifiable Risk Factors in RA: You Can Lead a Horse to Water but …, by Michael Vallis. The Great Debate: Treat Pre-RA vs Not Treat Pre-RA. Arguing for: Volodko Bakowsky and Derek Haaland; Against: Hani El-Gabalawy and Rob McDougall. The contributions presented at the meeting are reflected in the abstracts, which we are pleased to publish in this issue.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.472 | 0.280 |
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".