Canadian Rheumatology Association (CRA) Meeting, Quebec City, Quebec, February 3-6, 2010
Bibliographic record
Abstract
undergraduate education.He established the "bootcamp" elective in rheumatology that has developed locally and spread nationally.His passion and commitment to curriculum development in teaching rheumatology have earned him this most deserving award.Dr. Sasha Bernatsky of McGill University was the recipient of the Young Investigator Award for her research focusing on outcomes in rheumatic diseases including morbidity, mortality and the economic effects of conditions such as RA, juvenile idiopathic arthritis, SLE, and fibromyalgia.Much of her work has focused on a large international multicenter cohort study on cancer in SLE.Her investigations have also included assessing various risk factors for cancer in lupus using administrative databases in a variety of rheumatic diseases.The meeting concluded with the Dunlop-Dottridge lecture given by Prof. Iain McInnes, an authentic Glaswegian and experimental rheumatologist, who covered terrain from bench to bedside with his topic, "Finding new therapies in inflammatory arthritis: immune complexity or opportunity?".He presented his research focus on mechanisms of inflammatory synovitis in RA and psoriatic arthritis.His translational program encompassing basic cellular immunology through clinical trial interventions has extended to include the role of atherogenesis in autoimmunity.Finally, the CRA continues to ensure continuity of The Journal's mission as the Canadian-based international voice of rheumatology.Enthusiasm among the editorial team for our relationship with the CRA continues, as does our pride.We are CRA members contributing to The Journal as readers, reviewers, and authors, disseminating new information that benefits all our patients.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.228 | 0.051 |
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".