<i>Étude et Suivi:</i>Rheumatoid Arthritis in the 21st Century
Bibliographic record
Abstract
For over 20 years much research in rheumatoid arthritis (RA) has focused on early identification and treatment of patients to prevent longterm joint damage, disability, and morbidity. A multitude of studies have demonstrated that early intervention improves outcomes, and in particular, treatment during the so-called “window of opportunity,” when patients first develop inflammatory arthritis, may halt development of chronic symptoms altogether1,2. We have new classification criteria3, developed specifically to aid early identification of patients who are likely to need disease-modifying therapy, and a host of new biologic drugs, in particular anti-tumor necrosis factor therapies, that have revolutionized our ability to suppress disease activity4. So how close are we to achieving the aims of longterm remission and minimal disability in clinical practice? In this issue of The Journal , Combe, et al report on the 5 year outcomes of the well established French ESPOIR cohort5. The developers of the ESPOIR “ Étude et Suivi des POlyarthritis Indifferences Recentes ” cohort, established to study and monitor early undifferentiated polyarthritis, should be congratulated on establishing a large nationwide cohort of patients with early undifferentiated inflammatory arthritis, providing real-world data on the progression of RA over time. By monitoring patients seen in clinic at regular intervals … Address correspondence to Dr. Verstappen; E-mail: suzanne.verstappen{at}manchester.ac.uk
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".