Abstract C023: Studying early onset cancer: benefits and limitations of studies within the Military Health System
Bibliographic record
Abstract
Abstract Studies of the small numbers of younger cases in older existing cohorts will not allow us to clearly understand the factors associated with the rising rates of early onset cancers in the last 25 years. While clearly genetic susceptibility plays a role in the etiology of cancer, population genetics are not changing as rapidly as the rates of early onset cancers. We must make use of existing prospective data from young individuals, in the birth cohorts for whom cancer rates are increasing, to investigate contemporary exposures. Cancer rates have increased in the population of more than 1.3 million individuals in the U.S. military in parallel to the general population despite the required military health and fitness standards. This population and their linked data available in the Military Health System provides a valuable resource to investigate environmental exposures alone or in combination with genetic susceptibility and the associations with early onset cancers. The average age of this racially and ethically diverse population is ∼29 years old with those >30 years old increasing in recent decades. Although the cohort is predominantly male, females still make up about 18% of the active-duty force. For those on active duty, information collected during their service from the military medical, occupational, and pharmaceutical databases with analyses of serial serum samples, obtained approximately every two years since the late 1980’s, can be analyzed to identify factors that impact the risk of early onset cancers. Even if all information of interest may not be available and the number and volume of samples for each subject is limited, researchers can still glean a great deal from studies of this population. Considering the methodological factors of both calendar time in measured exposures and timing with respect to diagnosis allows for the potential identification of the relevant windows of susceptibility to specific exposures. The most appropriate study design and methods for implementation as well as the limitations to consider will be presented. Ongoing nested-case-control studies focusing on testicular, breast, colorectal, thyroid, and pancreatic cancers use these resources to study the environmental determinants of early onset cancers in this population. Access to these resources is currently available through collaboration with Department of Defense researchers. Plans are developing for wider access with future linkage with the national virtual pooled cancer registry that will enable identification of those diagnosed after leaving military service. The views expressed are those of the author and do not necessarily reflect the official views of the Uniformed Services University of the Health Sciences or the Department of Defense. Citation Format: Celia Byrne. Studying early onset cancer: benefits and limitations of studies within the Military Health System [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr C023.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".