Pancreatic Tumorigenesis: Precursors, Genetic Risk Factors and Screening
Bibliographic record
Abstract
Pancreatic cancer (PC) is a highly malignant and aggressive tumor. Despite medical advancement, the silent nature of PC results in only 20% of all cases considered resectable at the time of diagnosis. It is projected to become the second leading cause in 2030. Most pancreatic cancer cases are diagnosed in the advanced stages. Such cases are typically unresectable and are associated with a 5-year survival of less than 10%. Although there is no guideline consensus regarding recommendations for screening for pancreatic cancer, early detection has been associated with better outcomes. In addition to continued utilization of imaging and conventional tumor markers, clinicians should be aware of novel testing modalities that may be effective for early detection of pancreatic cancer in individuals with high-risk factors. The pathogenesis of PC is not well understood; however, various modifiable and non-modifiable factors have been implicated in pancreatic oncogenesis. PC detection in the earlier stages is associated with better outcomes; nevertheless, most oncological societies do not recommend universal screening as it may result in a high false-positive rate. Therefore, targeted screening for high-risk individuals represents a reasonable option. In this review, we aimed to summarize the pathogenesis, genetic risk factors, high-risk population, and screening modalities for PC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".