Association between food-based dietary inflammatory potential and ulcerative colitis: a case–control study
Bibliographic record
Abstract
Despite several studies on the link between dietary inflammatory potential and risk of several conditions, limited studies investigated the association between pro-inflammatory diet and ulcerative colitis (UC). The objective of the present study was to examine the link between food-based dietary inflammatory potential (FDIP) and odds of UC in Iranian adults. This case-control study was carried out among 109 cases and 218 randomly chosen healthy controls. UC was diagnosed and confirmed by a gastroenterologist. Patients with this condition were recruited from Iranian IBD registry. Age- and sex-matched controls were selected randomly from participants of a large cross-sectional study. Dietary data were obtained using a validated 106-item semi-quantitative food frequency questionnaire (FFQ). We calculated FDIP score using subjects' dietary intakes of 28 pre-defined food groups. In total 67% of subjects were female. There was no significant difference in mean age between cases and controls (39.5 vs. 41.5y; p = 0.12). The median (interquartile range) of FDIP scores for cases and controls were - 1.36(3.25) and - 1.54(3.15), respectively. We found no significant association between FDIP score and UC in the crude model (OR 0.93; 95% CIs 0.53-1.63). Adjustment for several potential confounders in multivariate model did not change this association (OR 1.12; 95% CIs 0.46-2.71). We failed to observe any significant association between greater adherence to a pro-inflammatory diet and risk of UC in this study. Prospective cohort studies are needed to further assess this relationship.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".