How Are Rheumatologists Managing Anticyclic Citrullinated Peptide Antibodies–positive Patients Who Do Not Have Arthritis?
Bibliographic record
Abstract
To the Editor: Early referral and initiation of disease-modifying antirheumatic drugs (DMARD) is associated with better outcomes for patients with rheumatoid arthritis (RA)1,2. In the United Kingdom, general practitioners (GP) are advised to refer patients with suspected RA urgently3 and rheumatology departments are rewarded for timely management of these patients4. Although a positive step, a corollary of this is that rheumatologists are now seeing patients earlier in the natural history of RA [e.g., patients with autoantibodies, especially anticyclic citrullinated peptide antibodies (anti-CCP) and symptoms but no clinical synovitis, who are at risk of developing RA]. This presents a clinical problem but also a significant opportunity. There is no evidence for the management of these (often symptomatic) at-risk individuals, but it is possible that the right intervention in this phase may prevent clinical arthritis5,6. This hypothesis is being explored in clinical trials (e.g., rituximab delayed, but did not prevent, arthritis onset in at-risk individuals)7. We were interested in … Address correspondence to Dr. K. Mankia, Academic Clinical Lecturer in Rheumatology, Leeds Institute of Rheumatic and Musculoskeletal Medicine, Chapel Allerton Hospital, Chapeltown Road, Leeds LS7 4SA, UK. E-mail: k.s.mankia{at}leeds.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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".