Racial and Geographic Disparities in Interhospital ICU Transfers*
Bibliographic record
Abstract
OBJECTIVES: Interhospital transfer, a common intervention, may be subject to healthcare disparities. In mechanically ventilated patients with sepsis, we hypothesize that disparities not disease related would be found between patients who were and were not transferred. DESIGN: Retrospective cohort study. SETTING: Nationwide Inpatient Sample, 2006-2012. PATIENTS: Patients over 18 years old with a primary diagnosis of sepsis who underwent mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We obtained age, gender, length of stay, race, insurance coverage, do not resuscitate status, and Elixhauser comorbidities. The outcome used was interhospital transfer from a small- or medium-sized hospital to a larger acute care hospital. Of 55,208,382 hospitalizations, 46,406 patients met inclusion criteria. In the multivariate model, patients were less likely to be transferred if the following were present: older age (odds ratio, 0.98; 95% CI, 0.978-0.982), black race (odds ratio, 0.79; 95% CI, 0.70-0.89), Hispanic race (odds ratio, 0.79; 95% CI, 0.69-0.90), South region hospital (odds ratio, 0.79; 95% CI, 0.72-0.88), teaching hospital (odds ratio, 0.31; 95% CI, 0.28-0.33), and do not resuscitate status (odds ratio, 0.19; 95% CI, 0.15-0.25). CONCLUSIONS: In mechanically ventilated patients with sepsis, we found significant disparities in race and geographic location not explained by medical diagnoses or illness severity.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".