Adherencia a una dieta pro-inflamatoria y asociación con el riesgo de cáncer gástrico en población adulta: Revisión sistemática de estudios observacionales.
Bibliographic record
Abstract
Abstract Introduction: Evidence suggests that adhering to a dietary inflammatory index (DII) may increase the adult population risk of various types of cancer. Aim: To examine the scientific material that has been published to date on the association between DII adherence and the risk of gastric cancer (GC). Methods: Systematic review of observational studies using the PubMed/MEDLINE, EMBASE and Scopus databases from January 2003 to January 2023. Observational studies on humans that examined exposure to an DII or its adaption and association with the GC were considered. Using the Newcastle Ottawa Scale (NOS), which is based on the PRISMA criteria, study quality and bias risk were evaluated. Results: 8 articles that met the elegibility criteria were selected. 3 prospective cohort studies and 5 case-control studies stood out among them. A strong positive association between following a pro-inflammatory DII and a higher risk of GC incidence was discovered in the majority of the studies examined (87.5%). According to 2 studies, a pro-inflammatory dietary pattern and the location of the proximal GC are positively correlated. The research that was examined by NOS reported a minimal risk of bias. Discussion: DII has significant advantages because to its adaptability and comparability across many groups, which is supported by a strong body of evidence used to validate it. Dietary interactions with variables such as H. pylori can have an effect on chronic inflammation of the mucosa of the gastric tissue. Conclusions: In several adult populations analyzed, pertinent epidemiological data were discovered that strongly suggests a association between following a dietary pattern that promotes inflammation and an elevated risk of gastric 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 | 0.038 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".