Health care utilisation before and after intensive care unit admission in rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVES: We evaluated the incidence of intensive care unit (ICU) admission in a rheumatoid arthritis (RA) population according to health care utilisation and use of immune therapies in the year preceding admission. Also, we compared health care utilisation after ICU admission in persons with and without RA. METHODS: We identified all persons with RA in Manitoba, Canada using population-based administrative data, and controls matched by age, sex, and region of residence. ICU admissions were identified using special care unit codes included in hospital discharge abstracts. We estimated the annual incidence rate of ICU admission in the RA population according to health care utilisation using generalised linear models, adjusting for age, sex, comorbidity, region and socioeconomic status. We compared health care utilisation post-ICU admission in persons with and without RA. RESULTS: From 2000/01 through 2009/10, the average annual incidence of ICU admission was 1.26% in the RA population. Corticosteroid use was associated with an increased incidence of ICU admission (IRR 1.07; 95%CI: 1.05, 1.09). Use of disease-modifying anti-rheumatic drugs and biologics was not associated with an increased incidence of ICU admission. In the year following ICU admission, 45.3% of the RA population was re-hospitalised, and 8.9% were readmitted to the ICU. CONCLUSIONS: Persons with RA who are admitted to the ICU have higher rates of health care utilisation in the year before ICU admission than those who are not admitted. Corticosteroid use is associated with an increased risk of ICU admission even after accounting for other health care utilisation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".