Antibiotics Should Be Used as First-line Therapy for Crohnʼs Disease
Bibliographic record
Abstract
The etiology of Crohn's disease remains uncertain, and to date no therapy is curative. Recent experimental evidence suggests that an altered immune response to commensal enteric flora in a genetically susceptible host plays a key role in both the development and perpetuation of the intestinal inflammation of Crohn's disease. Thus, incorporation of antibiotics into the therapeutic armamentarium for Crohn's disease, either as first-line therapy or combined with immunomodulatory drugs, would seem to be a rational strategy. Indeed, most IBD clinicians would attest to the marked benefit of antibiotic therapy in individual patients. Skepticism surrounding this approach arises because evidenced-based analyses' show that the few clinical trials evaluating the efficacy of antibiotics for Crohn's disease have produced equivocal or negative results or have methodological deficiencies, including small number of patients and absence of a placebo group. However, by undertaking an analysis that integrates information from both basic and clinical spheres of study, the dichotomy between experimental and clinical observations tends to merge. This approach underscores certain key factors that determine an optimal response to antibiotics, emphasizes the requirement for assessment in well-defined subsets of patients, and leads to the conclusion that antibiotics do provide benefit for Crohn's disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".