Ulcerative Colitis and Diverticulitis Are Possibly Connected to the Same Hub
Bibliographic record
Abstract
To the Editors, We read the report from Dr. Veloso titled, “Are Ulcerative Colitis and Diverticulitis Collected by the Same Hub?” with great interest. This is an interesting case that shares a similar clinical experience of developing de novo inflammatory bowel disease (IBD) after diverticulitis in a young patient. The described patient had an episode of complicated diverticulitis requiring surgical intervention. Seven years later, he developed ulcerative proctitis, which later progressed to symptomatic distal colitis. The current treatments for inflammatory bowel disease focus on mitigating the sequela of chronic inflammation; however, there remains a knowledge gap of complex environmental factors coupled with genetic susceptibility that interplays in the pathogenesis of IBD. There has been an increasing incidence of IBD and diverticulitis among the younger population in industrialized countries, suggesting shared environmental factors in the pathogenesis of these 2 clinical entities. Reduced dietary fiber intake, processed food, break-in mucosal integrity, and possible antibiotic exposure as part of complicated diverticulitis treatment contribute to gut microbiota dysbiosis and intestinal inflammation.1,2 Although our study did not include smoking cessation as one of the risk factors, multiple studies have shown that former smokers have a significantly high risk of ulcerative colitis development.3,4 Therefore, it is conceivable that smoking cessation could be one of the environmental determinants for IBD. As opposed to our study in which most patients had ulcerative pancolitis with some patients requiring biologics and surgical intervention, the presented case had distal colitis controlled with 5-ASA regimen. Still, it should be pointed out that ulcerative colitis has a relapsing and remitting course with nonexistent data on the long-term clinical course of IBD in patients who had a prior episode of complicated diverticulitis.5 We believe that with the evidence of growing literature, such as the case presented by Veloso et al, more extensive prospective studies with extended follow-up data and microbiome analysis would help to understand the risk factors and pathogenesis of IBD development after episodes of complicated diverticulitis. Identifying individuals at high risk for IBD development potentially allows close monitoring and early intervention to check disease progression.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".