Racial/Ethnic Disparities in Childhood Cancer Survival in the United States
Bibliographic record
Abstract
BACKGROUND: Non-white patients with childhood cancer have worse survival than Non-Hispanic (NH) White patients for many childhood cancers in the United States. We examined the contribution of socioeconomic status (SES) and health insurance on racial/ethnic disparities in childhood cancer survival. METHODS: We used the National Cancer Database to identify NH White, NH Black, Hispanic, and children of other race/ethnicities (<18 years) diagnosed with cancer between 2004 and 2015. SES was measured by the area-level social deprivation index (SDI) at patient residence and categorized into tertiles. Health insurance coverage at diagnosis was categorized as private, Medicaid, and uninsured. Cox proportional hazard models were used to compare survival by race/ethnicity. We examined the contribution of health insurance and SES by sequentially adjusting for demographic and clinical characteristics (age group, sex, region, metropolitan statistical area, year of diagnosis, and number of conditions other than cancer), health insurance, and SDI. RESULTS: < 0.05). Survival differences were attenuated after adjusting for health insurance and SDI separately; and further attenuated after adjusting for insurance and SDI together. CONCLUSIONS: Both SES and health insurance contributed to racial/ethnic disparities in childhood cancer survival. IMPACT: Improving health insurance coverage and access to care for children, especially those with low SES, may mitigate racial/ethnic survival disparities.
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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 | 0.004 |
| 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 | 0.000 | 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".