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 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 | 0.003 | 0.013 |
| 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 | 0.000 | 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".