Presence of Autoantibodies in Males and Females With Rheumatoid Arthritis: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is more common in females, and although the cause of RA is unknown, it is characterized by the production of autoantibodies. The aims of this study were to determine whether RA-associated autoantibodies are more often found in females than males and to identify factors that influence the relationship between sex and seropositivity. METHODS: Databases were searched and studies of RA (N ≥ 100) were included if they reported proportion of seropositive patients with RA by sex. Metaanalyses and metaregression were conducted using the random-effects model. Covariates regressed were smoking, age, BMI, Health Assessment Questionnaire-Disability Index (HAQ-DI), and the Disease Activity Score in 28 joints (DAS28). RESULTS: Eighty-four studies with a total of 141,381 subjects with rheumatoid factor (RF) seropositivity and 95,749 subjects with anticitrullinated protein antibody (ACPA) seropositivity met inclusion criteria. The mean age of participants ranged from 37 to 68 years and the proportion of female subjects ranged from 9% to 92%. Results indicated that females were less likely than males to be seropositive: odds ratio (OR) 0.84 [95% CI 0.77-0.91] for RF and OR 0.88 [95% CI 0.81-0.95] for ACPA. BMI, smoking, mean age, DAS28, and HAQ-DI did not affect the relationship between sex and seropositivity. CONCLUSION: Although studies report that females have higher RA disease activity than males and that seropositivity predicts worse outcomes, females were less likely to be seropositive than males.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".