Establishing a Clinic-Based Pancreatic Cancer and Periampullary Tumour Research Registry in Quebec
Bibliographic record
Abstract
BACKGROUND: Enrolling patients in studies of pancreatic ductal adenocarcinoma (pdac) is challenging because of the high fatality of the disease. We hypothesized that a prospective clinic-based study with rapid ascertainment would result in high participation rates. Using that strategy, we established the Quebec Pancreas Cancer Study (qpcs) to investigate the genetics and causes of pdac and other periampullary tumours (pats) that are also rare and underrepresented in research studies. METHODS: Patients diagnosed with pdac or pat were introduced to the study at their initial clinical encounter, with a strategy to enrol participants within 2 weeks of diagnosis. Patient self-referrals and referrals of unaffected individuals with an increased risk of pdac were also accepted. Family histories, epidemiologic and clinical data, and biospecimens were collected. Additional relatives were enrolled in families at increased genetic risk. RESULTS: The first 346 completed referrals led to 306 probands being enrolled, including 190 probands affected with pdac, who represent the population focus of the qpcs. Participation rates were 88.4% for all referrals and 89.2% for pdac referrals. Family history, epidemiologic and clinical data, and biospecimens were ascertained from 91.9%, 54.6%, and 97.5% respectively of patients with pdac. Although demographics and trends in risk factors in our patients were consistent with published statistics for patients with pdac, the qpcs is enriched for families with French-Canadian ancestry (37.4%), a population with recurrent germ-line mutations in hereditary diseases. CONCLUSIONS: Using rapid ascertainment, a pdac and pat research registry with high participation rates can be established. The qpcs is a valuable research resource and its enrichment with patients of French-Canadian ancestry provides a unique opportunity for studies of heredity in these diseases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".