Insurance Type and Withdrawal of Life-Sustaining Therapy in Critically Injured Trauma Patients
Bibliographic record
Abstract
Importance: Withdrawal of life-sustaining therapy (WLST) decisions for critically injured trauma patients are complicated and multifactorial, with potential for patients' insurance status to affect decision-making. Objectives: To determine if patient insurance type (private insurance, Medicaid, and uninsured) is associated with time to WLST in critically injured adults cared for at US trauma centers. Design, Setting, and Participants: This retrospective registry-based cohort study included reported data from level I and level II trauma centers in the US that participated in the American College of Surgeons Trauma Quality Improvement Program (TQIP) registry. Participants included adult trauma patients who were injured between January 1, 2017, and December 31, 2020, and required an intensive care unit stay. Patients were excluded if they died on arrival or in the emergency department or had a preexisting do not resuscitate directive. Analyses were performed on December 12, 2023. Exposures: Insurance type (private insurance, Medicaid, uninsured). Main Outcomes and Measures: An adjusted time-to-event analysis for association between insurance status and time to WLST was performed, with analyses accounting for clustering by hospital. Results: This study included 307 731 patients, of whom 160 809 (52.3%) had private insurance, 88 233 (28.6%) had Medicaid, and 58 689 (19.1%) were uninsured. The mean (SD) age was 40.2 (14.1) years, 232 994 (75.7%) were male, 59 551 (19.4%) were African American or Black patients, and 201 012 (65.3%) were White patients. In total, 12 962 patients (4.2%) underwent WLST during their admission. Patients who are uninsured were significantly more likely to undergo earlier WLST compared with those with private insurance (HR, 1.54; 95% CI, 1.46-1.62) and Medicaid (HR, 1.47; 95% CI, 1.39-1.55). This finding was robust to sensitivity analysis excluding patients who died within 48 hours of presentation and after accounting for nonwithdrawal death as a competing risk. Conclusions and Relevance: In this cohort study of US adult trauma patients who were critically injured, patients who were uninsured underwent earlier WLST compared with those with private or Medicaid insurance. Based on our findings, patient's ability to pay was may be associated with a shift in decision-making for WLST, suggesting the influence of socioeconomics on patient outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".