A Population-Based Study of the Epidemiology of Pancreatic Cancer: A Brief Report
Bibliographic record
Abstract
OBJECTIVE: Administrative data are used to describe the pancreatic cancer (pcc) population. The analysis examines demographic details, incidence, site, survival, and factors influencing mortality in a cohort of individuals diagnosed with pcc. METHODS: Incident cases of pcc diagnosed in Ontario between 1 January 2004 and 31 December 2011 were extracted from the Ontario Cancer Registry. They were linked by encrypted health card number to several administrative databases to obtain demographic and mortality information. Descriptive, bivariate, and survival analyses were conducted. RESULTS: During the period of interest, 9221 new cases of pcc (4548 in men, 4673 in women) were diagnosed, for an age-adjusted standardized annual incidence in the range of 8.6-9.5 per 100,000 population. Mean age at diagnosis was 70.3 ± 12.5 years (standard deviation). Five-year survival was 7.2% (12.8% for those <60 years of age and 3.6% for those >80 years of age). Survival varied by sex, older age, rural residence, lower income, site of involvement in the pancreas, and presence of comorbidity. CONCLUSIONS: The mortality rate in pcc is exceptionally high. With an increasing incidence and a mortality positively associated with age, additional support will be needed for this highly fatal disease as demographics in Ontario continue to trend toward a higher proportion of older individuals.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 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".