Racial and Ethnic Disparities in Survival in Lung Transplant Candidates with Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Minority patients have worse outcomes than nonminority patients in a variety of pulmonary diseases. We aimed to compare the survival of Black and Hispanic patients to that of others with idiopathic pulmonary fibrosis (IPF). We performed a retrospective cohort study of patients with IPF who were evaluated for lung transplantation at our center. Kaplan-Meier survival curves and Cox proportional hazards models were used to compare survival between groups. Black and Hispanic patients had spirometry, lung volumes and diffusion capacity that were similar to others, but had worse exercise capacity. Minority patients had a significantly increased risk of death compared to others independent of transplantation status (hazard ratio = 3.3, 95% CI 1.2-8.9, p = 0.02). Differences in exercise capacity, pulmonary hemodynamics and socioeconomic factors appeared to account for some of the differences in survival. Black and Hispanic patients with IPF had an increased risk of death following referral for lung transplantation. This finding may be due to differences in disease progression and/or differences in access to medical care among minority patients. Future studies should confirm our findings in a larger cohort. The elimination of racial and ethnic disparities in outcome should be a priority for clinicians and researchers in this field.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".