Role of race and ethnicity in survival among children/young adults with relapsed ALL: a Children’s Oncology Group report
Bibliographic record
Abstract
ABSTRACT: Pediatric Hispanic and Black patients with newly diagnosed B-cell acute lymphoblastic leukemia (B-ALL) experience worse overall survival (OS). We hypothesized that differential outcomes by race and ethnicity following relapse may contribute to disparities. We examined 2053 patients with ALL enrolled in frontline Children's Oncology Group trials from 1996 to 2014 who relapsed. We assessed the association of race and ethnicity, disease characteristics, and socioeconomic status with relapse survival predictors and postrelapse OS. For noninfant B-ALL, postrelapse OS (P = .002) and disease-related prognosticators such as time to relapse (P = .0002) differed by race and ethnicity. After adjusting for disease and patient characteristics, the OS association with overall race and ethnicity was attenuated, and lost statistical significance; Hispanic ethnicity specifically remained associated with worse OS (hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.01-1.41). Patients from highest annual median household income ZIP codes (>$85 000, approximately the highest quartile of patients) had better 5-year OS than those from the lowest (<$50 000; HR, 0.79; 95% CI, 0.63-0.99). Non-Hispanic Black and Hispanic patients more commonly lived in lower-income ZIP codes. For T-cell ALL, race, ethnicity, and socioeconomic status were not associated with OS. Worse postrelapse outcomes among racial and ethnic minority patients are largely driven by the prevalence of adverse disease-related factors at time of relapse, with a persistent disparity observed in Hispanic patients. The greatest impact in decreasing racial and ethnic B-ALL outcome disparities may be achieved by targeting frontline treatment interventions to address increased relapse among Black and Hispanic patients, and by developing and enabling equitable access to effective relapse treatments such as novel immunotherapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".