Canadian Rheumatology Association Meeting, February 17-20, 2016. Introduction, Abstracts, Author Index
Bibliographic record
Abstract
The 71st Annual Meeting of The Canadian Rheumatology Association (CRA) was held at the Fairmont Chateau Lake Louise, Lake Louise, Alberta, Canada, February 17–20, 2016. The program consisted of presentations covering original research, symposia, awards, and lectures. Highlights of the meeting include the following 2016 Award Winners: Distinguished Rheumatologist, Ronald Laxer; Distinguished Investigator, Proton Rahman; Teacher-Educator, Lori Albert; Young Investigator, Nigil Haroon; Best Abstract on Basic Science Research by a Trainee, Liam O’Neil; Best Abstract on Research by a Rheumatology Resident, Valérie Leclair; Best Abstract by a Medical Student, Matthew Jessome; Best Abstract by a Post-Graduate Resident, Hyein Kim; CRA/Arthritis Research Foundation (ARF) Best Epidemiology/Health Services Research Award, Cheryl Barnabe; Summer Studentship Mentor Award, Ines Colmegna; CRA/ARF Best Paediatric Research Award, Lily Lim; CRA/ARF Best Clinical Research Award, Zahi Touma; CRA/ARF Best Basic Science Research Award, Nigil Haroon; Best Abstract on SLE Research by a Trainee — Ian Watson Award, Stephanie Nantes; Best Abstract on Clinical or Epidemiology Research by a Trainee — Phil Rosen Award, Nicolas Richard; Practice Reflection Award — Gold/Silver/Bronze, Henry Averns/Philip Baer and Jean-Pierre Raynauld/Robert Ferrari. Lectures and other events included the Dunlop-Dottridge Lecture: New Paradigms for Inflammatory Arthritis, by Berent Prakken; Keynote Address by Distinguished Investigator Awardee 2016 Proton Rahman: Role of Precision Medicine in Optimizing Quality in Rheumatology Care; State of the Art Lecture: Everything You Must Know About Sleep but are Too Tired to Ask! by James Maas; and the Great Debate: Be it Resolved that Rheumatologists Get with the Times and Use Social Media for Communication with Patients and Research; arguing for: Jason Kur and Michelle Teo; against: Nadia Luca and Sherry Rohekar. Topics such as 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 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 | 0.005 |
| 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) | 0.471 | 0.290 |
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".