Bibliographic record
Abstract
Patients with inflammatory bowel disease (IBD) frequently ask nurses about what foods to eat or avoid to feel better. Historically, the role of diet as a treatment for IBD alone or in conjunction with medical therapy has been controversial. Although patients generally are given advice to eat a balanced diet, they continue to request more information about diet. The purpose of this study was to assess the reactions of people with IBD to foods consumed. A database was created to capture the season of data collection, the disease, the food, and the subject's reaction to each food. A 122-item food list was used. Sixty patients with IBD (n = 33 persons with Crohn's disease, n = 27 persons with ulcerative colitis) completed the questionnaire about foods and their reactions to the foods in the fall and spring representing summer and winter consumption. Foods that made the subjects feel better and worse were identified. Although the original purpose of the study was to assess people with IBD as a group, it became apparent that reactions to foods were different according to whether a subject had Crohn's disease or ulcerative colitis. Failure to distinguish between the two diseases and use only the pooled data made the data meaningless. The importance of this finding and themes related to foods that had a positive or negative effect on the subjects is discussed in this article.
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 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".