Redefining “dead on arrival”
Bibliographic record
Abstract
BACKGROUND: Significant variation exists across registries in the criteria used to identify patients with no chance of survival, with potential for profound impact on trauma center mortality. The purpose of this study was to identify the optimal case definition for the unsalvageable patient, for the purpose of exclusion from performance improvement (PI) endeavors. METHODS: Data were derived from the American College of Surgeons' Trauma Quality Improvement Program for 2012 to 2013. We proposed three potential case definitions for the unsalvageable patient: (1) no signs of life as determined by local providers (NSOL), (2) prehospital cardiac arrest (PHCA), and (3) a proxy definition (PROXY) based on presenting vital signs, defined as emergency department (ED) heart rate = 0, ED systolic blood pressure = 0, and Glasgow Coma Scale score motor component = 1. Case definitions were compared using standard predictive tests to determine specificity and positive predictive value (PPV) for in-hospital mortality. After the optimal definition was identified, hierarchical logistic regression was used to assess the impact of including unsalvageable patients on trauma center risk-adjusted mortality. The impact on trauma center performance was determined as change in outlier status and performance decile after exclusion of patients who met the optimal case definition. RESULTS: During the study period, 223,643 patients met inclusion criteria across 192 trauma centers. Overall in-hospital mortality was 7.2%. The PROXY definition had excellent PPV for death, with less than 1% of patients meeting the PROXY criterion surviving. By contrast, NSOL and PHCA had PPVs low enough such that many of these patients went on to live (33% and 10%, respectively). After exclusion of patients who met the PROXY definition, 7% of trauma centers changed performance decile. This change was greatest for patients with penetrating injury and shock, with change in performance decile at 23% and 33% of centers, respectively. CONCLUSION: The PROXY case definition has excellent predictive utility to identify patients who, based on presenting vital signs, will go on to die. PROXY should be used to exclude unsalvageable patients from PI endeavors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".