Dietary Factors Associated with Pancreatic Cancer Risk inMinia, Egypt: Principal Component Analysis
Bibliographic record
Abstract
Background: Pancreatic cancer (PC) is a serious and rapidly progressing malignancy. Identifying risk factors including dietary elements is important to develop preventive strategies. This study focused on possible links between diet and PC. Methods: We conducted a case-control study including all PC patients diagnosed at Minia Cancer Center and controls from general population from June 2014 to December 2015. Dietary data were collected directly through personal interviews. Principal component analysis (PCA) was performed to identify dietary groups. The data were analyzed using crude odds ratios (ORs) and multivariable logistic regression with adjusted ORs and 95% confidence intervals (CIs). Results: A total of 75 cases and 149 controls were included in the study. PCA identified six dietary groups, labeled as cereals and grains, vegetables, proteins, dairy products, fruits, and sugars. Bivariate analysis showed that consumption of vegetables, fruits, sugars, and total energy intake were associated with change in PC risk. In multivariable-adjusted models comparing highest versus lowest levels of intake, we observed significant lower odds of PC in association with vegetable intake (OR 0.24; 95% CI, 0.07-0.85, P=0.012) and a higher likelihood with the total energy intake (OR 9.88; 95% CI, 2.56-38.09, P<0.0001). There was also a suggested link between high fruit consumption and reduced odds of PC. Conclusions: The study supports the association between dietary factors and the odds of PC development in Egypt. It was found that higher energy intake is associated with an increase in likelihood of PC, while increased vegetable consumption is associated with a lower odds ratio.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".