A Population-based Study of Intensive Care Unit Admissions in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine the incidence of and mortality after critical illness in rheumatoid arthritis (RA) compared with the general population, and to describe the risks for and characteristics of critical illness in patients with RA. METHODS: We used population-based administrative data from the Data Repository at the Manitoba Centre for Health Policy from 1984 to 2010, and linked clinical data from an intensive care unit (ICU) database to identify all persons with RA in the province requiring ICU admission. We identified a population-based control group, matched by age, sex, socioeconomic status, and region of residence. The incidence of ICU admission, reasons for, and mortality after ICU admission were compared between populations using age- and sex-standardized rates, rate ratios, Cox proportional hazards models, and logistic regression models. RESULTS: We identified 10,078 prevalent and 5560 incident cases of RA. After adjustment, the risk for ICU admission was higher for RA (HR 1.65, 95% CI 1.50-1.83) versus the matched general population. From 2000-2010, the annual incidence of ICU admission among prevalent patients was about 1% in RA, with a crude 10-year incidence of 8%. Compared with the general population admitted to ICU, 1 year after ICU admission, mortality was increased by 40% in RA. Cardiovascular disorders were the most common reason for ICU admission in RA. CONCLUSION: Patients with RA have a higher risk for admission to the ICU than the general population and increased mortality 1 year after admission. Even with advances in management, RA remains a serious disease with significant morbidity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".