Predictors of ICU Admission and Outcomes 1 Year Post-Admission in Persons with IBD
Bibliographic record
Abstract
BACKGROUND: To determine predictors of intensive care unit (ICU) admission and to assess health care utilization (HCU) post-ICU admission among persons with inflammatory bowel disease (IBD). METHODS: We matched a population-based database of Manitobans with IBD to a general population cohort on age, sex, and region of residence and linked these cohorts to a population-based ICU database. We compared the incidence rates of ICU admission among prevalent IBD cases according to HCU in the year before admission using generalized linear models adjusting for age, sex, socioeconomic status, region, and comorbidity. Among incident cases of IBD who survived their first ICU admission, we compared HCU with matched controls who survived ICU admission. RESULTS: Risk factors for ICU admission from the year before admission included cumulative corticosteroid use (incidence rate ratio, 1.006 per 100 mg of prednisone; 95% confidence interval, 1.004-1.008) and IBD-related surgery (incidence rate ratio, 2.79; 95% confidence interval, 1.99-3.92). Use of immunomodulatory therapies within 1 year, or surgery for IBD beyond 1 year prior, were not associated with ICU admission. In those who used corticosteroids and immunomodulatory medications in the year before ICU admission, the use of immunomodulatory medications conferred a 30% risk reduction in ICU admission (incidence rate ratio, 0.70; 95% confidence interval, 0.50-0.97). Persons with IBD who survived ICU admission had higher HCU in the year following ICU discharge than controls. CONCLUSIONS: Corticosteroid use and surgery within the year are associated with ICU admission in IBD while immunomodulatory therapy is not. Surviving ICU admission is associated with high HCU in the year post-ICU discharge.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".