Autoantibodies to Tumor Necrosis Factor in Patients with Rheumatoid Arthritis and Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To detect autoantibodies to tumor necrosis factor (TNF) in patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), and to determine their clinical correlates. METHODS: Ninety-two patients with RA and 62 with SLE were studied. Sera were examined for autoantibodies to TNF by enzyme linked immunoassay. Levels of these autoantibodies were analyzed in respect to markers of inflammation such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and joint erosions, as well as other clinical, laboratory, and therapeutic aspects of RA and SLE. RESULTS: Anti-TNF levels were higher in those RA patients without erosions, but did not correlate with ESR or CRP. CONCLUSION: These observations suggest that autoantibody anti-TNF may be part of the innate immune system and may contribute to decreased inflammation in patients with RA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".