Determination of specificity and sensitivity of Anti-RA 33 in diagnosis of early Rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Rheumatoid arthritis is a chronic inflammatory disease with uncertain etiology characterized by symmetric polyarthritis in peripheral joints. Its diagnosis is based on clinical findings and serologic tests. They are rarely conclusive in early course of the disease. So, its early diagnosis could be difficult. The present study was designed to evaluate the role of Anti -RA33; an Auto-Antibody against RA33 in early diagnosis of the disease. MATERIALS & METHODS: forty three patients who had been visited in a Rheumatology Clinic were randomly selected. Their disease has been diagnosed by a Rheumatologist. 55 persons were chosen from healthy individuals who had attended in other clinic. Their age and sex were matched with the case group. Anti-RA33 and RF titers were measured in their blood sample using standard methods. FINDINGS: RF and Anti-RA33 titers had significant correlation in case group (p=0.015). Anti -RA33 test had 98% sensitivity, 20% specificity, 55% positive predictive value, and 90% negative predictive. CONCLUSION: Anti -RA33 could have diagnostic and prognostic value in diagnosis and evaluation of patients with RA, and its differentiation from other small joint disorders, particularly when the other serologic tests are negative.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".