RMTD-03 SOCIOECONOMIC DISPARITIES IN CLINICAL TRIAL ENROLLMENT AMONG PATIENTS WITH BRAIN METASTASES: A CENTRAL PENNSYLVANIA CROSS-SECTIONAL ANALYSIS IN NEIGHBORHOOD-DISADVANTAGE METRICS
Bibliographic record
Abstract
Abstract Socioeconomic disparities significantly influence health outcomes in patients with brain metastases (BM), yet their influence on clinical trial enrolment remain understudied. This cross-sectional analysis evaluated 1,802 patients (non-small cell lung cancer [NSCLC]: n=1,536; melanoma: n=182; HER2-positive breast cancer: n=84) treated at Penn State Cancer Institute (2006–2022) to assess associations between neighborhood disadvantage metrics, education, and clinical trial enrollment. Neighborhood disadvantage was measured via the Area Deprivation Index (ADI; national/state percentiles/deciles) and Community Human Development Index (CHDI). Logistic regression and Kaplan-Meier analyses examined predictors of enrollment and survival outcomes. An improved overall survival was noted in clinical trial enrollees versus non-enrollees across all patients (p=0.0043), without a significant association between ADI and survival. Subgroup analyses (NSCLC, melanoma, HER2+ breast cancer) showed no survival benefit linked to trial participation, likely related to the limited sample size in each cohort. Higher education (OR: 1.02, 95% CI: 1.00–1.03; p=0.02) significantly predicted increased trial enrollment in the combined cohort. Conversely, elevated national ADI (NADI; OR: 0.97, 95% CI: 0.96–0.99; p=0.007) and state ADI (SADI; OR: 0.85, 95% CI: 0.74–0.97; p=0.02) were associated with reduced enrollment, suggesting that residing in disadvantaged neighborhoods decreased access. Trends toward increased enrollment with higher life expectancy (p=0.072) and CHDI (p=0.068) were also observed. In melanoma patients, lower income was independently associated with enrollment (p=0.04). These findings underscore socioeconomic inequities in trial access, driven by education and neighborhood disadvantage, and highlight survival advantages for enrollees. Targeted interventions addressing structural barriers in disadvantaged communities are critical to improving equity in cancer care and research participation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".