Healthy Eating Index‐2015 as a predictor of ulcerative colitis risk in a case–control cohort
Bibliographic record
Abstract
OBJECTIVE: There is compelling clinical evidence implicating certain dietary components in the development and clinical course of progression in ulcerative colitis (UC). This study aimed to assess whether any association exists between ulcerative colitis and scores on a healthy eating index. METHODS: In this case-control study patients with UC were recruited and assessed together with healthy controls. The participants completed a validated 168-item food frequency questionnaire, the results of which were subsequently used to generate individual healthy eating index (HEI-2015) scores. RESULTS: Altogether 58 patients with UC and 123 healthy controls were recruited. After controlling for confounding factors, participants who were in the highest quartile of the HEI-2015 had a 66% lower odds ratio (OR) of UC than the lowest quartile (OR = 0.34, 95% confidence interval 0.12-0.96). CONCLUSION: HEI-2015 was associated with UC in this cohort. Further elucidation of the role of key dietary elements is now warranted.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".