Differential Inequality Trends Over Time in Survival Among U.S. Children with Acute Lymphoblastic Leukemia by Race/Ethnicity, Age at Diagnosis, and Sex
Bibliographic record
Abstract
BACKGROUND: It is unclear how inequalities in survival among children with acute lymphoblastic leukemia (ALL) have changed over time in different race/ethnicity groups. METHODS: Children diagnosed with a first primary malignant ALL at ages 0 to 19 years in 1975-2010 in the nine Surveillance, Epidemiology, and End Results cancer registries were included. Cumulative ALL mortalities were compared, and multivariable Cox regression analyses were applied to estimate ALL mortality HRs associated with race/ethnicity, age at diagnosis, and sex, adjusting for each other, within each diagnosis period (1975-1983, 1984-1991, 1992-1999, and 2000-2010). RESULTS: Compared with non-Hispanic-whites (NH-whites), the HR in non-Hispanic-blacks (NH-blacks) dropped to 1.21 [95% confidence interval (CI), 0.74-1.96] in 2000-2010 from the largest inequality in 1984-1991 (HR, 2.09; 95% CI, 1.57-2.79); the HR in Hispanics increased, however, from 1.28 (95% CI, 0.98-1.66) in 1975-1983 to 1.95 (95% CI, 1.48-2.58) in 2000-2010. Asian/Pacific Islanders (API) and American Indian/Alaska Natives (AIAN) had HRs of 1.39 (95% CI, 0.92-2.11) and 2.31 (95% CI, 1.13-4.74), respectively, in 2000-2010 with nonstatistically significant increases over time. In 2000-2010, compared with NH-white counterparts, NH-blacks and APIs diagnosed at 1-9 years, Hispanics diagnosed at 1-9 and 10-19 years, and AIANs diagnosed at 10-19 years all had about twice the ALL mortality hazard rates; inequality was observed among API boys (HR, 1.61; 95% CI, 1.00-2.60) but not API girls. CONCLUSIONS: Survival inequalities changed differently across subgroups of children with ALL. IMPACT: Underlying causes of the differential trends need to be examined, such that targeted interventions can be developed to reduce inequalities.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".