The Hereditary Spectrum of Pancreatic Cancer: The Edmonton Experience
Bibliographic record
Abstract
OBJECTIVE: Pancreatic cancer is known to aggregate in some families and has been associated with a wide variety of cancer syndromes. The authors describe their experience with pancreatic cancer and the range of associated cancer syndromes. METHODS: The charts of all patients seen for concern of a hereditary cancer syndrome in the Cancer Genetics Clinic at the University of Alberta between 1995 and 2002 were reviewed. RESULTS: Forty families reported a personal or family history of pancreatic cancer in the context of a possible hereditary cancer syndrome. Three additional families reported a history of pancreatitis. Twenty-four (56%) of those families were suspected of having a hereditary breast and ovarian cancer syndrome. A further seven (16%) were suspected of having hereditary nonpolyposis colon cancer. Only three (7%) were believed to be at risk for a site-specific pancreatic cancer syndrome. Another three (7%) were suspicious for hereditary pancreatitis. The remaining family histories were suggestive of Li-Fraumeni syndrome, von Hippel-Lindau syndrome or a nonspecific cancer predisposition. CONCLUSIONS: With such a wide variety of hereditary cancer syndromes associated with pancreatic cancer, an accurate assessment of the family history is essential to determine the most appropriate cancer screening for at-risk family members and to guide any molecular testing that may be offered.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".