The Prevalence of Rheumatoid Arthritis in Chile: A Nationwide Study Performed as Part of the National Health Survey
Bibliographic record
Abstract
OBJECTIVE: Genetic and environmental backgrounds influence the development of rheumatoid arthritis (RA). In Latin America, epidemiologic data are scarce. We aimed to determine the prevalence of RA in Chile in a population-based study. METHODS: The National Health Survey was a cross-sectional household survey with a stratified multistage probability sample of 6233 participants performed between August 2016 and March 2017. A screening instrument for RA was applied to a random sample of 3847 subjects > 30 years old. Positive screening was defined by at least 1 of the following: 2 swollen joints for at least 4 consecutive weeks (past/present), and/or a diagnosis of arthritis in the past. Individuals with positive screening had rheumatoid factor, anticitrullinated protein antibodies, and C-reactive protein measured, as well as clinical examination performed by a rheumatologist. Self-report of doctor-diagnosed RA was also performed. RESULTS: The screening questionnaire was applied to 2998 subjects. A positive screening was found for 783 (22.1%). Among subjects with positive screening, 493 (66%) had a clinical evaluation performed by a rheumatologist. Using the American College of Rheumatology/European League Against Rheumatism 2010 classification criteria, prevalence was 0.6% (95% CI 0.3-1.2). Prevalence was higher in women, and 3.3% of subjects self-reported having RA. CONCLUSION: According to this national population-based study, RA prevalence in Chile is 0.6% (0.3-1.2), a value similar to what has been found in developed countries and slightly lower than some Latin American countries. Self-reporting leads to overestimating RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".