Should Quantitative Measures and Management of Rheumatoid Arthritis Include More Than Control of Inflammatory Activity?
Bibliographic record
Abstract
To the Editor: We agree strongly with Kremer et al that “metrics are essential for evaluating disease activity in patients with rheumatoid arthritis (RA).”1 Nonetheless, data reported from the Corrona and the Brigham and Women’s Rheumatoid Arthritis Sequential Study (BRASS) registries for Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) are quite similar to those reported in the initial 2008 RAPID3 report.2 In the Corrona, BRASS, and 2008 RAPID3 databases, mean scores, respectively, for CDAI were 11.2, 19.5, and 12.3; RAPID3 8.2, 7.6, and 8.7; correlations of CDAI and RAPID3 0.72, 0.58, and 0.74; κ 0.24, 0.24, and 0.32; and weighted κ 0.49, 0.39, and 0.51 (Table 1). The proportion of patients in remission (REM) or low disease activity (LDA) in the 3 databases, respectively, were 60%, 39%, and 51% for CDAI, and 46%, 48%, and 51% for RAPID3 (Table 1). View this table: Table 1. Measures and indices to assess patients and RA in 3 databases (2 reported in 2021 and 1 in 2008). Similar results may certainly be interpreted differently by different observers, and we respect that others may find “significant disparities”1 between indices that we suggested give similar results.2 At the same time, … Address correspondence to Dr. T. Pincus, Rush University, 1611 West Harrison Street, Suite 510, Chicago, IL 60612, USA. Email: tedpincus{at}gmail.com.
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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 | 0.017 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".