Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic immune-related disorder that affects gastrointestinal tract. As the disease is non-curable, patients need a lifelong treatment with a typical disease course of remissions and relapses.1–3 The etiopathogenesis of IBD is still unclear; however, many identified and non-identified environmental factors play a major role in intestinal dysbiosis with subsequent immune dysregulation in genetically susceptible individuals.1–4 Several environmental factors such as nicotine smoking, early exposure to breastfeeding, antibiotics, air pollution, and rurality have been examined. One of the major environmental determinants in IBD pathogenesis is diet.4,5 Several dietary factors have been explored and linked to the pathogenesis of IBD such as vitamin D intake.5–7 Obesity, on the other hand, has been recognized as a major health problem with a concerning surge in prevalence, especially in Western countries. Obesity is known to be associated with an inflammatory state and its link to IBD has been the subject of ongoing basic and clinical research over the last several years.8,9 Obese persons with IBD may have a higher rate of disease relapse, resistance to medical therapy, and the need for IBD-related surgery.10,11 In an interesting review, Szilagyi et al12 compared rates of obesity, IBD, with the geographic markers of lactase digestion status, average population-weighted national latitude, and national yearly sunshine exposure in 47 countries across the globe. The main findings shown were the global modest to moderate correlations of Crohn’s disease (CD) and ulcerative colitis (UC) incidence with geographic markers. The correlation between obesity and IBD (both CD and UC) was globally moderate, but it was poor for Europe and Asia. As the obesity pandemic is more pronounced in Western countries, it is not clear why the correlation was poor in Europe. The correlation of CD incidence with lactase non-persistence (LNP) in Europe and the correlation of CD and UC incidence with latitude in Asia remained moderate to strong. When the prevalence data on IBD were assessed, the outcomes with the geographical markers were similar globally but less clear in Europe and Asia, where UC prevalence remains moderately associated with LNP and strongly associated with latitude in Asia. It is difficult to know what this exactly means or the implications of these findings. While the study is interesting, it is limited by the availability and accuracy of epidemiological data from different countries. The results of the study are dependent on several assumptions and calculations that might be difficult to interpret. The accuracy of these calculations needs to be validated in future studies. Data availability: Data sharing is not applicable to this article, as no new data were created or analyzed in this article. Conflict of Interests: W.E-M. served as an advisory board member for Janssen and AbbVie and MERCK Canada.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".