Dietary inflammatory index and risk of reflux oesophagitis, Barrett’s oesophagus and oesophageal adenocarcinoma: a population-based case–control study
Bibliographic record
Abstract
The dietary inflammatory index (DIITM) is a novel composite score based on a range of nutrients and foods known to be associated with inflammation. DII scores have been linked to the risk of a number of cancers, including oesophageal squamous cell cancer and oesophageal adenocarcinoma (OAC). Given that OAC stems from acid reflux and that the oesophageal epithelium undergoes a metaplasia-dysplasia transition from the resulting inflammation, it is plausible that a high DII score (indicating a pro-inflammatory diet) may exacerbate risk of OAC and its precursor conditions. The aim of this analytical study was to explore the association between energy-adjusted dietary inflammatory index (E-DIITM) in relation to risk of reflux oesophagitis, Barrett's oesophagus and OAC. Between 2002 and 2005, reflux oesophagitis (n 219), Barrett's oesophagus (n 220) and OAC (n 224) patients, and population-based controls (n 256), were recruited to the Factors influencing the Barrett's Adenocarcinoma Relationship study in Northern Ireland and the Republic of Ireland. E-DII scores were derived from a 101-item FFQ. Unconditional logistic regression analysis was applied to determine odds of oesophageal lesions according to E-DII intakes, adjusting for potential confounders. High E-DII scores were associated with borderline increase in odds of reflux oesophagitis (OR 1·87; 95 % CI 0·93, 3·73), and significantly increased odds of Barrett's oesophagus (OR 2·05; 95 % CI 1·22, 3·47), and OAC (OR 2·29; 95 % CI 1·32, 3·96), when comparing the highest with the lowest tertiles of E-DII scores. In conclusion, a pro-inflammatory diet may exacerbate the risk of the inflammation-metaplasia-adenocarcinoma pathway in oesophageal carcinogenesis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".