Childhood cancer survivorship research in minority populations: A position paper from the Childhood Cancer Survivor Study
Bibliographic record
Abstract
By the middle of this century, racial/ethnic minority populations will collectively constitute 50% of the US population. This temporal shift in the racial/ethnic composition of the US population demands a close look at the race/ethnicity-specific burden of morbidity and premature mortality among survivors of childhood cancer. To optimize targeted long-term follow-up care, it is essential to understand whether the burden of morbidity borne by survivors of childhood cancer differs by race/ethnicity. This is challenging because the number of minority participants is often limited in current childhood cancer survivorship research, resulting in a paucity of race/ethnicity-specific recommendations and/or interventions. Although the overall childhood cancer incidence increased between 1973 and 2003, the mortality rate declined; however, these changes did not differ appreciably by race/ethnicity. The authors speculated that any racial/ethnic differences in outcome are likely to be multifactorial, and drew on data from the Childhood Cancer Survivor Study to illustrate the various contributors (socioeconomic characteristics, health behaviors, and comorbidities) that could explain any observed differences in key treatment-related complications. Finally, the authors outlined challenges in conducting race/ethnicity-specific childhood cancer survivorship research, demonstrating that there are limited absolute numbers of children who are diagnosed and survive cancer in any one racial/ethnic minority population, thereby precluding a rigorous evaluation of adverse events among specific primary cancer diagnoses and treatment exposure groups. Cancer 2016;122:2426-2439. © 2016 American Cancer Society.
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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 | 0.044 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".