The Value of HLA-DRB1 Shared Epitope, −308 Tumor Necrosis Factor-α Gene Promoter Polymorphism, Rheumatoid Factor, Anti-Citrullinated Peptide Antibodies, and Early Erosions for Predicting Radiological Outcome in Recent-Onset Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To study the value of HLA-DRB1 shared epitope (SE), -308 tumor necrosis factor-alpha (TNF-alpha) gene promoter polymorphism, rheumatoid factor (RF), anti-citrullinated peptide antibodies (anti-CCP), and baseline erosions for predicting radiological outcome at 1 year in patients with recent-onset rheumatoid arthritis (RA). METHODS: Radiological damage was assessed by radiographs at baseline and at 1 year in an inception cohort of 134 RA patients with disease duration<or=1 year at study entry. Radiographs were scored with the modified Sharp/van der Heijde (SvdH) erosion score for hands, wrists, and feet. The predictive value of the variables was studied by multiple linear regression analysis, using immunogenetic factors, baseline SvdH erosion score, and type of treatment during the followup period as independent variables, and SvdH erosion score at 1 year as the dependent variable. RESULTS: The SvdH erosion score increased from the baseline visit to the 1-year visit in 49 patients (36.6%). In multiple linear regression analysis, radiological outcome was significantly predicted by SE homozygosity (beta coefficient 1.75; 95% CI 1.54, 2.96; p=0.005) and baseline SvdH erosion score (beta coefficient 1.56; 95% CI 1.4, 1.71; p<0.001). This model explained 78% of the variability of the dependent variable (R2=0.779). CONCLUSION: Erosive damage at 1 year in patients with recent-onset RA is significantly influenced by SE homozygosity and the presence of baseline erosions, but not by RF status, anti-CCP status, or -308 TNF-alpha genotype.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".