Disparities in Access to Lung Transplantation for Patients with Cystic Fibrosis by Socioeconomic Status
Bibliographic record
Abstract
RATIONALE: Although previous studies suggest that access to care for patients with cystic fibrosis (CF) does not vary appreciably by socioeconomic status (SES), disparities with respect to access to lung transplantation for patients with CF are largely unknown. OBJECTIVES: To determine whether access to lung transplantation for patients with CF differs according to SES. METHODS: Observational study involving 2,167 adult patients with CF from the CF Foundation Patient registry who underwent their first lung transplant evaluation between 2001 and 2009. The primary outcome was acceptance for lung transplant after initial evaluation. The main SES indicator was Medicaid status. Alternate SES indicators included race, educational attainment, ZIP code-level median household income, and driving time from residence to closest lung transplant center. MEASUREMENTS AND MAIN RESULTS: The odds that Medicaid recipients were not accepted for lung transplant were 1.56-fold higher (95% confidence interval [CI], 1.27-1.92) than patients without Medicaid, after multivariate adjustment for demographic characteristics, disease severity, and potential contraindications to lung transplant, and before or after use of the lung allocation score. This association was independent of other SES indicators, including race, educational attainment, ZIP code-level median household income, and driving time to closest transplant center (odds ratio [OR] = 1.37; 95% CI, 1.10-1.72). Patients not completing high school (OR = 2.37; 95% CI, 1.49-3.79) and those residing in the lowest (vs. highest) ZIP code median household income category (OR = 1.39; 95% CI, 1.01-1.93) also experienced a higher odds of not being accepted for lung transplant in multivariate analysis. CONCLUSIONS: In this nationally representative study of adult patients with CF, multiple indicators of low SES were associated with higher odds of not being accepted for lung transplant.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".