Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it