The burden of illness of rheumatoid arthritis in Latin America—A systematic literature review
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease which, when left untreated, may result in the destruction of multiple joints and damage a wide variety of body systems, including the skin, eyes, lungs, heart, and blood vessels. The objective of this study was to conduct a systematic review of disease burden for RA in Argentina, Brazil, Colombia, Mexico, and Venezuela. PubMed/Medline, Embase, and Web of Science were searched for publications in English, Spanish, or Portuguese from 2008 through June 2018. A total of 1700 records were retrieved and 36 articles were included. The estimated prevalence of RA for these countries ranged from 0.15% (Colombia) to 2.8% (Mexico). The Global Burden of Disease initiative 2019 estimated that RA accounted for 0.13% of world disability-adjusted life-years. For Latin America, these figures were higher: Argentina 0.16%, Brazil 0.16%, Colombia 0.21%, Mexico 0.30%, and Venezuela 0.24%. RA has a negative impact on physical, mental, and emotional well-being as shown by substantially lower scores on measures of quality of life (SF-36) compared with the general population. The annual direct cost in Mexico was estimated at US$3599 per person. For patients with severe RA in Brazil these costs were approximately US$10 000. Data from other studied countries were similar. Though evidence of the full cost and impact of RA in Latin American countries is scarce and additional studies are needed, the burden of RA in these regions is significant and comparable to other parts the world.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".