The association between dietary zinc intake and risk of pancreatic cancer: a meta-analysis
Bibliographic record
Abstract
Previous reports have suggested a potential association on dietary zinc intake with the risk of pancreatic cancer. Since the associations between different studies were controversial, we therefore conducted a meta-analysis to reassess the relationship between dietary zinc intake and pancreatic cancer risk. A comprehensive search from the databases of PubMed, Embase, Web of Science, and Medline was performed until January 31, 2017. Relative risk (RR) with 95% confidence intervals (CI) derived by using random effect model was used. Sensitivity analysis and publication bias were conducted. Our meta-analysis was based on seven studies involving 1659 cases, including two prospective cohort studies and five case–control studies. The total RR of pancreatic cancer risk for the highest versus the lowest categories of dietary zinc intake was 0.798 (0.621–0.984), with its significant heterogeneity among studies (I2=58.2%, P=0.026). The average Newcastle–Ottawa scale (NOS) score was 7.29, suggesting a high quality. There was no publication bias in the meta-analysis about dietary zinc intake on the risk of pancreatic cancer. Subgroup analyses showed that dietary zinc intake could reduce the risk of pancreatic cancer in case–control studies and among American populations. In conclusion, we found that highest category of dietary zinc intake can significantly reduce the risk of pancreatic cancer, especially among American populations.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".