High Spicy Food Intake and Risk of Cancer
Bibliographic record
Abstract
BACKGROUND:: Studies on the association between spicy food intake and cancer risk have reported inconsistent results. We quantitatively assessed this association by conducting a meta-analysis based on evidence from case-control studies. METHODS:: PubMed, EMBASE, and the Cochrane Library were searched for eligible publications. Combined odds ratios (OR s) with their 95% confidence interval (CI) were calculated using a random- or fixed-effects model. The methodological quality of the included articles was assessed using the Newcastle-Ottawa scale (NOS). All data were analyzed using STATA 11.0 software (version 11.0; StataCorp., College Station, TX, USA). Subgroup analyses were also performed with stratification by region, sex, number of cases, cancer subtype, source of the control group, and NOS score. RESULTS:: A total 39 studies from 28 articles fulfilled the inclusion criteria for the meta-analysis (7884 patients with cancer and 10,142 controls). Comparison of the highest versus lowest exposure category in each study revealed a significant OR of 1.76 (95% CI = 1.35-2.29) in spite of significant heterogeneity (P < 0.001). In the subgroup analyses, this positive correlation was still found for gastric cancer, different regions, different numbers of cases, different sources of the control group, and high-quality articles (NOS score of ≥ 7). However, no statistically significant association was observed for women, esophageal cancer, gallbladder cancer, or low-quality articles (NOS score of <7). No evidence of publication bias was found. CONCLUSIONS:: Evidence from case-control studies suggested that a higher level of spicy food intake may be associated with an increased incidence of cancer despite significant heterogeneity. More studies are warranted to clarify our understanding of the association between high spicy food intake and the risk of cancer.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".