Canadian Rheumatology Association Meeting, February 8-11, 2017. Introduction, Abstracts, Author Index
Bibliographic record
Abstract
The 72nd Annual Meeting of The Canadian Rheumatology Association (CRA) was held at The Westin Ottawa, Ottawa, Ontario, Canada, February 8−11, 2017. The program consisted of presentations covering original research, symposia, awards, and lectures. Highlights of the meeting include the following 2017 award winners: Dr. Vinod Chandran, Young Investigator; Dr. Jacques P. Brown, Distinguished Investigator; Dr. David Robinson, Teacher-Educator; Dr. Michel Zummer, Distinguished Rheumatologist; Ms. Rebecca Gole, Best Abstract on SLE Research by a Trainee − Ian Watson Award; Ms. Bailey Russell, Best Abstract on Clinical or Epidemiology Research by a Trainee − Phil Rosen Award; Dr. Sahil Koppikar and Dr. Henry Averns, Practice Reflection Award; Dr. Shirine Usmani, Best Abstract on Basic Science Research by a Trainee; Ms. Carol Dou, Best Abstract for Research by an Undergraduate Student; Dr. Dania Basodan, Best Abstract on Research by a Rheumatology Resident; Dr. Claire Barber, Best Abstract on Adult Research by Young Faculty; Ms. Audrea Chen, Best Abstract by a Medical Student; Dr. Kun Huang, Best Abstract by a Post-Graduate Resident; and Dr. Ryan Lewinson, Best Abstract by a Post-Graduate Research Trainee. Lectures and other events included a Keynote Lecture by Jonathon Fowles: Exercise is Medicine: Is Exercise a Good or Bad Thing for People with Arthritis?; State of the Art Lecture by Matthew Warman: Insights into Bone Biology and Therapeutics Gleaned from the Sustained Investigation of Rare Diseases; Dunlop-Dottridge Lecture by Allen Steere: Lyme Disease: A New Problem for Rheumatologists in Canada; and the Great Debate: Be it Resolved that the Least Expensive Treatment Should be Chosen. Switch, Switch, Switch! Arguing for: Jonathan Chan and Antonio Avina, and against: Marinka Twilt and Glen Hazlewood. Topics such as rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, Sjogren syndrome, psoriatic arthritis, spondyloarthritis, vasculitis, osteoarthritis, fibromyalgia, pediatric rheumatology, 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.513 | 0.324 |
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".