Protein-protein interaction in pancreatic cancer
Bibliographic record
Abstract
Pancreatic cancer leads as the most lethal malignant neoplasm in the developing world [1]. The two main types are adenocarcinoma and endocrine tumors, accounting for 85% and 5% of pancreatic cancers respectively [1]. Multiple risk factors are associated with pancreatic cancer such as recreational drug use, smoking, alcohol, genetics, diabetes, physical exercise and age. The disease affects men slightly more than women (~5% more) as age increases [1]. Research on the origins of this cancer has been eluded due to the absence of experimental procedures on humans that violate ethical standards in the U.S and Canada. Experiments on mouse models may serve as a guide but are limited. Firstly, cross-cultural correlational data collected on patients are inadequate due to confounding demographic variables that are inconsistent between different groups such as socioeconomic status, social networks, and access to healthcare. Prevention and Future Goals: Due to lack of screening procedures for prevention it may be beneficial in identifying risk factors to help reduce chances of getting pancreatic cancer. Also, community and state-wide fundraising programs can help incapable families obtain access to expensive chemotherapeutic drugs, such as COX-inhibitors that have reported potential activity against pancreatic cancer [1] Facts & Figures [1,2,3] - Pancreatic cancer is the 7th leading cause of death globally and accounting for 4% of all deaths due to cancer. - Occurrence in men: 1 in 74 will develop pancreatic cancer and 1 in 72 will die due pancreatic cancer. - Occurrence in Women: 1 in 72 will develop pancreatic cancer and 1 in 66 will die due to pancreatic cancer. - 5-year survival rate is <5%. Resources: - American Cancer Society - Canadian Cancer Society - Support Pancreatic Cancer in Canada - Pancreatic Cancer Action Network
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".