Bibliographic record
Abstract
When The Journal of Rheumatology was first launched in 1974, our founder, Metro Ogryzlo, always had the intention of promoting rheumatology research and supporting the rheumatology community. For over 50 years, we have accomplished this through the regular publication of high-quality articles as well as abstracts and proceedings, and by providing unique features, such as our Expert Reviews and Panorama articles, to our readers. We have always prided ourselves in publishing content that will educate and give clinicians around the world the tools to help patients with rheumatic disease. In this light, I would like to introduce a new original section that will soon appear in The Journal that I believe will further give back to the rheumatology community. When the managing editor, Carlye Rodrigo, and I attended both the Pan American League of Associations for Rheumatology (PANLAR) and Asia Pacific League of Associations for Rheumatology (APLAR) conferences recently, as well as in discussions we have had with the Association of Women in Rheumatology, we noticed … Address correspondence to Dr. E.D. Silverman, Editor-in-Chief, The Journal of Rheumatology, 365 Bloor Street East, Suite 901, Toronto, ON M4W 3L4, Canada. Email: esilverman{at}jrheum.com.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".