Clinician predictions of intensive care unit mortality*
Bibliographic record
Abstract
OBJECTIVE: Predicting outcomes for critically ill patients is an important aspect of discussions with families in the intensive care unit. Our objective was to evaluate clinical intensive care unit survival predictions and their consequences for mechanically ventilated patients. DESIGN: Prospective cohort study. SETTING: Fifteen tertiary care centers. PATIENTS: Consecutive mechanically ventilated patients > or = 18 yrs of age with expected intensive care unit stay > or = 72 hrs. INTERVENTIONS: We recorded baseline characteristics at intensive care unit admission. Daily we measured multiple organ dysfunction score (MODS), use of advanced life support, patient preferences for life support, and intensivist and bedside intensive care unit nurse estimated probability of intensive care unit survival. MEASUREMENTS AND MAIN RESULTS: The 851 patients were aged 61.2 (+/- 17.6, mean + SD) yrs with an Acute Physiology and Chronic Health Evaluation (APACHE) II score of 21.7 (+/- 8.6). Three hundred and four patients (35.7%) died in the intensive care unit, and 341 (40.1%) were assessed by a physician at least once to have a < 10% intensive care unit survival probability. Independent predictors of intensive care unit mortality were baseline APACHE II score (hazard ratio, 1.16; 95% confidence interval, 1.08-1.24, for a 5-point increase) and daily factors such as MODS (hazard ratio, 2.50; 95% confidence interval, 2.06-3.04, for a 5-point increase), use of inotropes or vasopressors (hazard ratio, 2.14; 95% confidence interval, 1.66-2.77), dialysis (hazard ratio, 0.51; 95% confidence interval, 0.35-0.75), patient preference to limit life support (hazard ratio, 10.22; 95% confidence interval, 7.38-14.16), and physician but not nurse prediction of < 10% survival. The impact of physician estimates of < 10% intensive care unit survival was greater for patients without vs. those with preferences to limit life support (p < .001) and for patients with less vs. more severe organ dysfunction (p < .001). Mechanical ventilation, inotropes or vasopressors, and dialysis were withdrawn more often when physicians predicted < 10% probability of intensive care unit survival (all ps < .001). CONCLUSIONS: Physician estimates of intensive care unit survival < 10% are associated with subsequent life support limitation and more powerfully predict intensive care unit mortality than illness severity, evolving or resolving organ dysfunction, and use of inotropes or vasopressors.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".