The Mayo Cardiac Intensive Care Unit Admission Risk Score is Associated with Medical Resource Utilization During Hospitalization
Bibliographic record
Abstract
Objective To determine whether the Mayo Cardiac Intensive Care Unit (CICU) Admission Risk Score (M-CARS) is associated with CICU resource utilization. Patients and Methods Adult patients admitted to our CICU from 2007 to 2018 were retrospectively reviewed, and M-CARS was calculated from admission data. Groups were compared using Wilcoxon test for continuous variables and χ 2 test for categorical variables. Results We included 12,428 patients with a mean age of 67±15 years (37% female patients). The mean M-CARS was 2.1±2.1, including 5890 (47.4%) patients with M-CARS less than 2 and 644 (5.2%) patients with M-CARS greater than 6. Critical care restricted therapies were frequently used, including mechanical ventilation in 28.0%, vasoactive medications in 25.5%, and dialysis in 4.8%. A higher M-CARS was associated with greater use of critical-care therapies and longer CICU and hospital length of stay. The low-risk cohort with M-CARS less than 2 was less likely to require critical-care–restricted therapies, including invasive or noninvasive mechanical ventilation (8.0% vs 46.1%), vasoactive medications (10.1% vs 38.8%), or dialysis (1.0% vs 8.2%), compared with patients with M-CARS greater than or equal to 2 (all P <.001). Conclusion Patients with M-CARS less than 2 infrequently require critical-care resources and have extremely low mortality, suggesting that the M-CARS could be used to facilitate the triage of critically ill cardiac patients.
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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 | 0.005 |
| 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".