Racial, Ethnic, and Sex-based Disparities among High-risk Individuals Undergoing Pancreatic Cancer Surveillance
Bibliographic record
Abstract
Since its inception two years ago, the international, multicenter Pancreatic Cancer Early Detection (PRECEDE) Consortium has enrolled high-risk individuals (HRI) undergoing pancreatic ductal adenocarcinoma (PDAC) surveillance. Herein we aim to evaluate enrollment disparities in PRECEDE. Data on HRIs enrolled between May 2020 and March 2022 were collected, with HRIs defined as participants enrolled in PRECEDE meeting guideline-based criteria for PDAC surveillance. Of 1,273 HRIs enrolled, 1,113 were eligible for inclusion, with 47.2% meeting familial pancreatic cancer criteria without a known pathogenic variant (PV) and the remainder having a pathogenic variant in a PDAC-risk gene (CDKN2A, STK11, PRSS1, BRCA1, BRCA2, PALB2, ATM, MLH1, MSH2, MSH6, PMS2, or EPCAM). Study participants were predominantly from the United States (82.7%), the most common age range at enrollment was 60-69 years (37.4%), and a non-PDAC cancer was present in 32.4%. There were racial/ethnic- and sex-based disparities among enrolled subjects, as the majority of participants were female (65.9%) and self-reported white (87.7%), with only 2.9% having Hispanic ethnicity. While more than 97% of participants consented to utilize imaging data and biosamples for research, there was no difference in rate of consent based on race/ethnicity, sex, or age, thereby demonstrating uniform participation in research activities among all subgroups after enrollment. Ensuring that diversity of HRIs in PDAC surveillance programs mirrors the communities served by participating centers is important. Substantial racial/ethnic- and sex-based disparities persist among recently enrolled HRIs undergoing PDAC surveillance, and therefore reducing these disparities will be a major focus of the PRECEDE Consortium moving forward. PREVENTION RELEVANCE: Pancreatic cancer surveillance is critical to decreasing pancreatic cancer mortality; therefore, it is important that pancreatic cancer surveillance studies enroll diverse patients. We demonstrate that substantial racial/ethnic- and sex-based disparities exist amongst enrollment in the international PRECEDE consortium, highlighting the dire need for future efforts to reduce these disparities. See related Spotlight, p. 305.
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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 | 0.004 |
| 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 | 0.000 |
| 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".