The Assessment of Risk in Cardiothoracic Intensive Care ( <scp>ARC</scp> t <scp>IC</scp> ): prediction of hospital mortality after admission to cardiothoracic critical care
Bibliographic record
Abstract
The models used to predict outcome after adult general critical care may not be applicable to cardiothoracic critical care. Therefore, we analysed data from the Case Mix Programme to identify variables associated with hospital mortality after admission to cardiothoracic critical care units and to develop a risk-prediction model. We derived predictive models for hospital mortality from variables measured in 17,002 patients within 24 h of admission to five cardiothoracic critical care units. The final model included 10 variables: creatinine; white blood count; mean arterial blood pressure; functional dependency; platelet count; arterial pH; age; Glasgow Coma Score; arterial lactate; and route of admission. We included additional interaction terms between creatinine, lactate, platelet count and cardiac surgery as the admitting diagnosis. We validated this model against 10,238 other admissions, for which the c index (95% CI) was 0.904 (0.89-0.92) and the Brier score was 0.055, while the slope and intercept of the calibration plot were 0.961 and -0.183, respectively. The discrimination and calibration of our model suggest that it might be used to predict hospital mortality after admission to cardiothoracic critical care units.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".