Incidence and Prevalence of Polymyalgia Rheumatica and Giant Cell Arteritis in a Healthcare Management Organization in Buenos Aires, Argentina
Bibliographic record
Abstract
OBJECTIVE: To estimate incidence and prevalence of polymyalgia rheumatica (PMR) and giant cell arteritis (GCA) in a university hospital-based health management organization (Hospital Italiano Medical Care Program) in Argentina. METHODS: Overall and sex-specific incidence rates (IRs) and prevalence were calculated (age ≥ 50 yrs). Incidence study followed members with continuous affiliation ≥ 1 year from January 2000 to December 2015. Diagnosis as per the 2012 European Alliance of Associations for Rheumatology/American College of Rheumatology (ACR) criteria for PMR or the ACR 1990 criteria for GCA. Prevalence was calculated on January 1, 2015. RESULTS: There were 176,558 persons who contributed a total of 1,046,620 person-years (PY). Of these, 825 developed PMR, with an IR (per 100,000 PY) of 78.8 (95% CI 73.4-84.2) overall, 90.1 (95% CI 82.9-97.2) for women, and 58.9 (95% CI 51.1-66.6) for men. Ninety persons developed GCA; the IR was 8.6 (95% CI 6.8-10.4) overall, 11.1 (95% CI 8.5-10.6) for women, and 4.2 (2.2-6.3) for men. There were 205 prevalent PMR cases and 23 prevalent GCA cases identified from a population of 80,335. Prevalence of PMR was 255 per 100,000 (95% CI 220-290) overall, 280 (95% CI 234-325) for women, and 209 (95% CI 150-262) for men; and the prevalence of GCA was 28.6 per 100,000 (95% CI 16.9-40.3) overall, 36.4 (95% CI 20.1-52.8) for women, and 14.2 (95% CI 0.3-28.1) for men. CONCLUSION: This is the first study of incidence and prevalence of PMR and GCA in Argentina. There were similarities and differences with cohorts from other parts of the world, but population-based epidemiologic studies in Latin America are needed.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".