Bibliographic record
Abstract
Despite major progress in diagnosis and treatment of rheumatoid arthritis (RA), this systemic autoimmune disorder is still a challenge for predicting the course of disease, therapeutic response, and outcome on the individual level. This is no surprise: the pathogenesis of RA is extremely complex, involving genetic predisposition, gender bias, dys-regulation of immunologic tolerance, and environmental factors, to name a few factors. Finally, an infiltrating army of various immune-competent cells induces production and release of proinflammatory cytokines as well as proteinases, inevitably causing destruction of cartilage and bone. On the other hand, our skills and opportunities to stop this process have significantly improved through introduction of biologic disease-modifying antirheumatic drugs. However, it again became evident that RA is not uniform, and new efforts have been made to identify markers for stratification of disease, also allowing prediction of response. As an example, encouraging data have come from clinical trials using rituximab, where seropositive patients (positive for rheumatoid factor and/or antibodies against citrullinated antigens) were characterized as the subgroup responding better to therapy1,2,3. Recently, a stronger reduction in disease activity under tumor necrosis factor (TNF) blockers was observed in seronegative patients with RA in a multicenter study in the UK4, and this unexpected finding was also confirmed in Italian and German cohorts. Since biomarkers to predict response are available not only from the autoantibody profile (as part of the immunome), but may also be encoded in the genes (the genome) or provided by the composition of mRNA expression (the transcriptome), proteins, and metabolites (proteome and metabolome), the field for future research … Address correspondence to Dr. Burmester. E-mail: gerd.burmester{at}charite.de
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".