Review article: medical therapy for fistulizing Crohn's disease
Bibliographic record
Abstract
BACKGROUND: Fistulae will develop in approximately one-third of patients with Crohn's disease. With an expected spontaneous healing rate of only 10%, fistulizing Crohn's disease requires a comprehensive strategy with a medical and possible surgical approach. AIM: To summarize the current literature evaluating various medical options for treating patients with fistulizing Crohn's disease. METHODS: A literature review was conducted using PubMed (search terms: Crohn's disease and fistula) and manual search of references among the identified studies and relevant review papers to identify papers that present data on medical treatment of fistulizing Crohn's disease. RESULTS: The first line of medical therapy remains antibiotics (metronidazole and ciprofloxacin). Mercaptopurine and azathioprine are medications that are effective in treating fistulizing Crohn's disease. The current gold standard of medical treatment to induce and maintain remission for fistulizing Crohn's disease is infliximab. Used as induction therapy, infliximab produced a 62% clinical response, and a complete closure rate of 46%. A maintenance therapy trial demonstrated at 54 weeks, 46% of patients receiving infliximab continued to respond to treatment, compared with 23% in the placebo group (P = 0.001). CONCLUSION: Further research to find new therapies and to improve our existing medical treatment of fistulizing Crohn's disease is required.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".