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Record W2780201537 · doi:10.1097/ta.0000000000001775

Examining racial disparities in the time to withdrawal of life-sustaining treatment in trauma

2017· article· en· W2780201537 on OpenAlexaff
Melissa A. Hornor, James P. Byrne, K Engelhardt, Avery B. Nathens

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2017
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersAmerican College of Surgeons
KeywordsInterquartile rangeMedicineOdds ratioUnivariate analysisPercentileAfrican americanLogistic regressionMultivariate analysisAsian americansGerontologyDemographyInternal medicineEthnic group

Abstract

fetched live from OpenAlex

BACKGROUND: Racial disparities in medical treatment for seriously injured patients across the spectrum of care are well established, but racial disparities in end of life decision making practices have not been well described. When time from admission to time to withdrawal of life-sustaining treatment (WLST) increases, so does the potential for ineffective care, health care resource loss, and patient and family suffering. We sought to determine the existence and extent of racial disparities in late WLST after severe injury. METHODS: We queried the American College of Surgeons' Trauma Quality Improvement Program (2013-2016) for all severely injured patients (Injury Severity Score, > 15; age, > 16 years) with a WLST order longer than 24 hours after admission. We defined late WLST as care withdrawn at a time interval beyond the 75th percentile for the entire cohort. Univariate and multivariate analyses were performed using descriptive statistics, and t tests and χ tests where appropriate. Multivariable regression analysis was performed with random effects to account for institutional-level clustering using late WLST as the primary outcome and race as the primary predictor of interest. RESULTS: A total of 13,054 patients from 393 centers were included in the analysis. Median time to WLST was 5.4 days (interquartile range, 2.6-10.3). In our unadjusted analysis, African-American patients (10.1% vs. 7.1%, p < 0.001) and Hispanic patients (7.8% vs. 6.8%, p < 0.001) were more likely to have late WLST as compared to early WLST. After adjustment for patient, injury, and institutional characteristics, African-American (odds ratio, 1.42; 95% confidence interval, 1.21-1.67) and Hispanic (odds ratio, 1.23; 95% confidence interval, 1.04-1.46) race were significant predictors of late WLST. CONCLUSION: African-American and Hispanic race are both significant predictors of late WLST. These findings might be due to patient preference or medical decision making, but speak to the value in assuring a high standard related to identifying goals of care in a culturally sensitive manner. LEVEL OF EVIDENCE: Prognostic and epidemiologic study, level III.

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)0.001
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 score0.179
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.036
GPT teacher head0.341
Teacher spread0.305 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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