S1676 Comparing Top-Down and Step-Up Strategies in the Treatment of IBD: An Updated Meta-Analysis of Remission and Surgical Outcomes
Bibliographic record
Abstract
Introduction: Inflammatory bowel disease (IBD), comprising crohn’s disease (CD) and ulcerative colitis (UC), is a chronic, progressive condition associated with cumulative bowel damage and frequent need for surgical intervention. Traditional step-up therapy delays advanced treatment until complications arise, whereas emerging evidence suggests that early initiation of biologics or immunomodulators may improve remission and reduce surgery risk. This has prompted interest in top-down strategies; however, pooled comparisons between top-down and step-up approaches remain limited. Methods: A comprehensive literature search was performed using PubMed and Google Scholar to identify relevant randomized controlled trials (RCTs) and observational studies reporting remission (clinical and endoscopic) and surgical outcomes. Study quality was assessed using the Cochrane Risk of Bias tool for RCTs and the Newcastle-Ottawa Scale for observational studies. Data was extracted independently, and pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using a random-effects model. Statistical heterogeneity was evaluated using the I² statistic. Results: A total of 7 studies met inclusion criteria, encompassing patients with both CD and UC. For remission, pooled data from 2692 patients receiving top-down therapy and 2214 receiving step-up therapy showed no significant difference between strategies (OR: 1.11; 95% CI: 0.46–2.67; I² = 97%; P = 0.81). In contrast, surgical outcomes favored top-down therapy, with pooled data from 7308 patients in the top-down group and 6451 in the step-up group demonstrating a significantly lower risk of surgical intervention (OR: 0.65; 95% CI: 0.48-0.88; I² = 61%; P = 0.005). Conclusion: In this meta-analysis, top-down therapy was associated with a significantly lower risk of surgical intervention compared to step-up therapy, suggesting a potential long-term benefit of early biologic initiation in IBD. However, no significant difference was observed in clinical remission rates between the 2 strategies. These findings underscore the need to individualize treatment decisions, considering both disease severity and patient-specific factors. The substantial heterogeneity underscores the need for further prospective, standardized studies to clarify the optimal sequencing of therapy in IBD management.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.075 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".