Hospitalizations in Sarcoidosis: A Cohort Study of a Universal Healthcare Population
Bibliographic record
Abstract
Abstract Rationale Population-based analyses of hospitalization rates from countries with universal healthcare systems are lacking for patients with sarcoidosis. Objectives To evaluate the long-term trends in hospitalization rates and risk factors for hospitalization in patients with sarcoidosis in Ontario, Canada. Methods We performed a cohort study using health administrative data from Ontario, Canada, between 1996 and 2015. Patients with sarcoidosis were identified by using two or more physician visits and International Classification of Diseases codes. All-cause and sarcoidosis-related hospitalization rates were age and sex standardized. Hospitalization rates between groups were analyzed by using Cochran-Armitage and Breslow-Day tests. Associations between patient characteristics and hospitalization rates were evaluated by using multivariable Poisson regression. Results In total, 18,550 individuals with sarcoidosis experienced 33,516 all-cause and 1,725 sarcoidosis-related hospitalizations. Adjusted all-cause hospitalization rates decreased from 206.4 to 152.1 per 1,000 cases between 1996 and 2015 (26% decrease; P < 0.001). The largest decrease in all-cause hospitalization occurred in patients 18–25 years old (67% decrease; P < 0.001). Adjusted sarcoidosis-related hospitalization rates decreased from 21.8 to 4.2 per 1,000 cases between 1996 and 2015 (81% decrease; P < 0.001). The decrease in sarcoidosis-related hospitalizations was largest in women compared with men (87% vs. 72%; P = 0.004) and in those 26–35 years old (91% reduction; P < 0.001). Lower income (risk ratio, 1.27 [1.18–1.37]; P < 0.001) and rural residence (risk ratio, 1.16 [1.08–1.24]; P < 0.001) were associated with increased all-cause hospitalizations. Conclusions Hospitalization rates in patients with sarcoidosis have decreased over the past 20 years, most substantially in patients of younger age. Important differences in the risk of hospitalization exist on the basis of sex, socioeconomic factors, and geographic factors in patients with 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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".