The Clinical and Research Implications of Anti-carbamylated Protein Antibodies
Bibliographic record
Abstract
Autoantibodies play a central role in the clinical management of rheumatoid arthritis (RA). They can be used for diagnostic as well as prognostic purposes. For many years, autoantibody testing in RA was limited to rheumatoid factor (RF), but in the late 1990s, Schellekens and colleagues confirmed that autoantibodies reactive to citrullinated peptides were highly specific for RA1. The identification of antibodies to citrullinated peptide/protein antigens (ACPA) and the commercialization of ACPA testing in the form of anticyclic citrullinated peptide (CCP) antibody assays has revolutionized the field of rheumatology. The magnitude of the role of autoantibodies in RA is reflected in the current 2010 RA classification criteria in which autoantibodies can account for up to 3 of the 6 points (50%) needed to classify inflammatory arthritis as RA2. In the clinical management of patients with RA, earlier diagnosis and initiation of appropriate treatment is associated with improved longterm outcomes3. While there are many factors that influence delays in diagnosis and treatment of RA, autoantibodies are one factor that can influence these clinical outcomes. For example, Pratt and colleagues found that in patients referred to an early arthritis clinic who were ultimately diagnosed with RA, patients who were ACPA- and RF-negative had a significant delay in the time to treatment following assessment by a rheumatologist4. This finding highlights the diagnostic uncertainty that rheumatologists often face when patients with inflammatory … Address correspondence to Dr. M.K. Demoruelle, University of Colorado School of Medicine, Division of Rheumatology, 1775 Aurora Court, Mail Stop B-115, Aurora, Colorado 80045, USA. E-mail: Kristen.Demoruelle{at}UCDenver.edu
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".