MétaCan
Menu
Back to cohort
Record W2285890598 · doi:10.1097/ta.0000000000000843

Redefining “dead on arrival”

2015· article· en· W2285890598 on OpenAlexaff
James P. Byrne, Wei Xiong, David Gómez, Stephanie Mason, Paul J. Karanicolas, Sandro Rizoli, Homer Tien, Avery B. Nathens

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2015
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreSt. Michael's Hospital
Fundersnot available
KeywordsHistory

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.360
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Trauma: Injury, Infection, and Critical CareSame topicTrauma and Emergency Care StudiesFrench-language works237,207