Pancreatic Cancer Genetic Epidemiology Consortium
Bibliographic record
Abstract
We have organized the Pancreatic Cancer Genetic Epidemiology (PACGENE) Consortium to identify susceptibility genes in familial pancreatic cancer (FPC). The Consortium comprises seven data collection centers, a statistical genetics core, and a pathology/archival genotyping core. We recruit kindreds containing two or more affected blood relatives ascertained through incident pancreatic adenocarcinoma cases, physician referrals, and/or through Internet recruitment. Accrual to a database containing core clinical, demographic, lifestyle, and family history information from questionnaires is ongoing, along with biospecimen collection. To date, 13,147 patients have been screened for family history, of whom 476 (50% male) probands and 1,912 of their adult (99% unaffected) relatives have been enrolled. Of these, 379 kindreds meet criteria for FPC, having at least two first-degree relatives with pancreatic cancer. Cumulative incidence curves using available age of diagnosis (onset) among and affected relatives were compared with those for incident pancreatic cancer cases reported to 13 U.S. Surveillance Epidemiology and End Results (SEER) sites from 1973 to 2000 (N = 72,700). The mean age +/- SD at diagnosis among 466 PACGENE probands and 670 affected relatives was 64.1 +/- 11.8 and was 65.4 +/- 11.6 for the subset of 369 FPC probands and 429 relatives. Both samples were significantly younger than the mean age at diagnosis in the SEER population (70.0 +/- 12.1 years; differences in curves versus SEER, P < 0.001). Age at diagnosis (excluding probands) in FPC kindreds does not decrease with increasing number of affected individuals. In our sample, younger age at diagnosis was observed whether we grouped probands by recruitment sites that predominantly recruited through high-risk referrals, or through screening all pancreatic cancer patients for family history. Linkage studies are ongoing. The PACGENE Consortium will be a valuable family-based resource that will greatly enhance genetic epidemiology research in pancreatic cancer.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".