Cancer in American Indian and Alaska Native young adults (ages 20-44 years): US, 1999-2004
Bibliographic record
Abstract
BACKGROUND: An examination of cancer incidence patterns in American Indians and Alaska Native (AI/AN) young adults may provide insight into their present and future cancer burden. METHODS: To reduce racial misclassification, incidence data were linked with the Indian Health Service (IHS) patient services database. Age-adjusted cancer incidence rates per 100,000 (AAR) and corresponding rate ratios (RR) for young adults (ages 20-44 years) were compared across IHS regions and for selected cancers within Contract Health Service Delivery Area counties by race (AI/AN vs non-Hispanic whites [NHW]) and sex. RESULTS: The all-sites cancer incidence rate was lower for AI/ANs (AAR of 83.8) than for NHWs (AAR of 111.2) (RR of 0.75) but varied by IHS regions. Among the leading cancers in AI/AN females the risk was elevated for stomach (RR of 3.22), colorectal (RR of 1.30), uterine (RR of 1.61), and kidney (RR of 1.39) cancers and was lower for breast (RR of 0.70) and thyroid (RR of 0.71) cancers. Among AI/AN young adult males the risk was elevated for stomach (RR of 2.62), liver (RR of 1.89), and kidney (RR of 1.59) cancers and lower for testicular germ cell cancer (RR of 0.64) and lymphoma (RR of 0.60). The risk for these and other cancers varied across IHS regions. CONCLUSIONS: Many of the cancer patterns that characterize the AI/AN population overall are apparent among young adults. Compared with NHW young adults, the overall cancer burden among AI/AN young adults was lower but varied for selected cancers and across IHS regions. Cancer control and research strategies are needed to address the unique genetic, social, cultural, and lifestyle aspects of AI/AN young adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".