50th Year of Publication: Honoring the Duncan A. Gordon Award Winners (Part 2)
Bibliographic record
Abstract
This month we are highlighting the Duncan A. Gordon award-winning papers from 2019 to 2022. This issue concludes not only 2023 but also the 50th anniversary year of publication of The Journal of Rheumatology . In 2019, there was a tie for the Duncan A. Gordon award and 2 papers were chosen: “Malignancies in patients with anti-RNA polymerase III antibodies and systemic sclerosis: analysis of the EULAR Scleroderma Trials and Research Cohort and possible recommendations for screening” by Lazzaroni et al1 and “B cell depletion therapy normalizes circulating follicular Th cells in primary Sjögren syndrome” by Verstappen et al.2 The paper by Lazzaroni et al, on behalf of the European Alliance of Associations for Rheumatology (EULAR) Scleroderma Trials and Research Cohort (EUSTAR), examined 176 of 4986 (3.5%) patients in their systemic sclerosis cohort who had anti-RNA polymerase III (RNAP3) antibodies only without any other autoantibodies. The overall rate of cancer was significantly higher in the anti-RNAP3+ cohort as compared to the other cohort (17.7% vs 9%) and the difference was more marked within the first 2 years from … 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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.137 | 0.114 |
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".