Increased Vitamin C Intake Is Associated With Decreased Pancreatic Cancer Risk
Bibliographic record
Abstract
Background: Patients with pancreatic cancer have an unfavorable 5-year survival rate of approximately 3% due to diagnosis occurring at advanced stages. Prior research has proposed vitamin C may have a therapeutic and preventative role in pancreatic cancer. Methods: A Health Insurance Portability and Accountability Act (HIPAA) compliant national database was utilized to assess pancreatic cancer risk in patients with or without a history of vitamin C intake. The International Classification of Diseases (ICD) codes were used, specifically the International Classification of Diseases, 10th Edition (ICD-10) and International Classification of Diseases, Nineth Edition (ICD-9), between January 2010 and December 2020. Patients were matched, and statistical analyses were implemented. Chi-squared, logistic regression, and odds ratio were used to test for significance and to estimate relative risk. Results: A total of 83,941 patients were identified as utilizing prescribed vitamin C. Subsequent matching by Charlson Comorbidity Index (CCI) score and age resulted in two groups of 50,384 patients. The incidence of pancreatic cancer was 243 (0.48%) in the group with a history of vitamin C intake compared to 442 (0.88%) in the control group. The difference was statistically significant by P < 3.174 ¡Á 10 -14 with an odds ratio of 0.548 (95% confidence interval (CI): 0.468 - 0.641). Overall, patients without vitamin C prescription had an increased prevalence of pancreatic cancer throughout all ages and regions of the United States when compared to those with a vitamin C prescription. In addition, healthcare costs were higher in total for the control group when compared to the experimental group. Conclusions: This retrospective cohort study found a statistically significant correlation between vitamin C and subsequent incidence of pancreatic cancer. Further studies are recommended to explore vitamin C¡¯s redox and cofactor activity in the context of preventing and possibly treating pancreatic cancer, as well as consider pancreatic cancer lifestyle risk factors such as smoking. World J Oncol. 2024;15(4):543-549 doi: https://doi.org/10.14740/wjon1854
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".