Racial, ethnic, and sex-based disparities among high-risk individuals undergoing pancreatic cancer surveillance.
Bibliographic record
Abstract
687 Background: The international, multi-center Pancreatic Cancer Early Detection (PRECEDE) Consortium enrolls high-risk individuals (HRIs) undergoing pancreatic ductal adenocarcinoma (PDAC) surveillance. Enrollment began in 2020, and despite challenges related to the COVID-19 pandemic, the PRECEDE Consortium rapidly accrued a large cohort of HRIs. The purpose of this study is to describe the characteristics of this cohort and assess racial, ethnic, and sex-based disparities. Methods: The PRECEDE Consortium (NCT04970056) is a prospective, multicenter study focused on improving survival from PDAC through early detection. Data from all HRIs who met criteria for PDAC surveillance and enrolled between May 2020 - March 2022 were collected and included in the analysis. Results: During the study period, 1299 HRIs enrolled in PRECEDE at 32 centers. HRIs were excluded if enrollment data was incomplete or criteria for PDAC surveillance were not met. Of 1113 who were included, 47.2% met criteria for familial pancreatic cancer (FPC) and 45.4% had a family history of PDAC along with a PV in a PDAC-risk gene (BRCA1, BRCA2, PALB2, ATM, MLH1, MSH2, MSH6, PMS2, or EPCAM). The remainder had familial atypical mole melanoma syndrome (5.7%), Peutz-Jeghers syndrome (1.6%), or hereditary pancreatitis (0.2%). More females than males enrolled (65.9% vs. 33.5%). The distribution of HRIs by race and ethnicity is depicted; the majority identified as white (87.7%). Study participants were primarily from the US (82.7%), the median age was 61 (27-85) and 18.5% had Ashkenazi Jewish ancestry. Nearly all HRIs consented to allow access to imaging data (99.6%), collection of germline DNA (97.7%), and biosample collection (99.5%). There were no race, ethnicity, or sex-based differences in rates of consent for collection of imaging, DNA, or biosamples. Conclusions: Enrollment of HRIs in prospective studies of PDAC surveillance is essential for advancing early detection research in PDAC. A distinct advantage of the PRECEDE Consortium for examining enrollment disparities is that recruitment began in 2020, providing a unique and current snapshot of the international PDAC surveillance landscape. Despite the recent attention on addressing disparities in healthcare delivery, significant racial, ethnic, and sex-based disparities persisted in the cohort of HRIs enrolled in the PRECEDE Consortium. Ensuring that the diversity of participants in the PRECEDE Consortium mirrors the communities served by participating centers is crucial. Further examining and addressing the reasons for these disparities is a major focus of the PRECEDE Consortium moving forward. [Table: see text]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".