Epidemiology and health outcomes of sarcoidosis in a universal healthcare population: a cohort study
Bibliographic record
Abstract
Sarcoidosis-related mortality appears to be rising in North America, with increasing rates in females and the elderly. We aimed to estimate trends in sarcoidosis incidence, prevalence and mortality in Ontario, Canada. We performed a cohort study using health administrative data from Ontario between 1996 and 2015. International Classification of Diseases and Ontario Health Insurance Plan codes were used for case detection. Three disease definitions were created: 1) sarcoidosis, two or more physician claims within 2 years; 2) chronic sarcoidosis, five or more physician claims within 3 years; and 3) sarcoidosis with histology, two or more physician claims with a tissue biopsy performed between claims. Overall, 18 550, 9199 and 3819 individuals with sarcoidosis, chronic sarcoidosis and sarcoidosis with histology, respectively, were identified. The prevalence of sarcoidosis was 143 per 100 000 in 2015, increasing by 116% (p<0.0001) from 1996. The increase in age-adjusted prevalence was higher in males than females (136%versus99%; p<0.0001). The incidence of sarcoidosis declined from 7.9 to 6.8 per 100 000 between 1996 and 2014 (15% decrease; p=0.0009). A 30.3% decrease in incidence was seen among females (p<0.0001) compared with a 5.5% increase in males (p=0.47). Age- and sex-adjusted mortality rates of patients with sarcoidosis rose from 1.15% to 1.47% between 1996 and 2015 (28% increase; p=0.02), with the overall trend being nonsignificant (p=0.39). Mortality rates in patients with chronic sarcoidosis increased significantly over the study period (p=0.0008). The prevalence of sarcoidosis is rising in Ontario, with an apparent shifting trend in disease burden from females to males. Mortality is increasing in patients with chronic sarcoidosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".