A Pooled Analysis of Second Primary Pancreatic Cancer
Bibliographic record
Abstract
Studies of pancreatic cancer in the setting of second primary malignant neoplasms can provide etiologic clues. An international multicenter study was carried out using data from 13 cancer registries with a registration period up to year 2000. Cancer patients were followed up from the initial cancer diagnosis, and the occurrence of second primary malignant neoplasms was compared with expected values derived from local rates, adjusting for age, sex, and period of diagnosis. Results from individual registries were pooled by use of a fixed-effects model. People were at higher risk of developing pancreatic cancer within 10 years of a diagnosis of cancers of the pharynx, stomach, gallbladder, larynx, lung, cervix, corpus uteri, bladder, and eye and 10 years or later following a diagnosis of cancers of the stomach, colon, gallbladder, breast, cervix, placenta, corpus uteri, ovary, testis, bladder, kidney, and eye, as well as Hodgkin's and non-Hodgkin's lymphomas. Pancreatic cancer was connected with smoking-related cancers, confirming the etiologic role of tobacco. The associations with uterine and ovarian cancers suggest that reproductive factors might be implicated in pancreatic carcinogenesis. The elevated pancreatic cancer risk in young patients observed among several types of cancer implies a role of genetic factors. Radiotherapy is also suggested as a risk factor.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".