Are clinical outcomes in IBD improved by multidisciplinary clinics?
Bibliographic record
Abstract
Chronic inflammatory bowel disease (IBD), consisting of Crohn's disease (CD) and ulcerative colitis (UC), has an incidence of 5 cases per 100,000 population. Despite recent progress in therapy, IBD follows a course of relapses and remissions, with ≈25%–50% of patients relapsing annually.1 Many patients face multiple problems with these diseases, such as a constant need for information about therapy, medications, nutrition, psychosocial issues, and possible surgical solutions, requiring a multidisciplinary approach.2 In some large centers this has resulted in the formation of multidisciplinary clinics, consisting of some or all of the following experts: gastroenterologists, nurse-educators, psychologists, dietitians, pharmacologists, and colorectal surgeons. The patients can see these experts either in 1 clinic visit or after referral to 1 or more of these disciplines. In addition, patients seen in specialist IBD clinics were provided better care than in nonspecialist clinics.3 Education of IBD patients is crucial for several reasons. At first diagnosis the patient needs to understand that IBD is chronic, needing regular follow-up and requiring compliance with medications administered for treatment as well as to prevent relapses. The nurse or nurse practitioner is an essential part of a multidisciplinary team to supply the patient with this education. Waters et al4 have shown that educated IBD patients display better compliance and visit emergency room less frequently than patient who did not receive formal education. However, formal education did not improve quality of life. Education will also instruct patients when to call about complications of IBD, such as when abscesses, intestinal obstructions, and fistulas appear and whenever the patient does not respond to therapy.
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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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".