Efficacy of Advanced Therapies as Prophylaxis and for Active Disease in Postoperative Crohn’s Disease: A Comprehensive Review
Bibliographic record
Abstract
Postoperative recurrence (POR) in Crohn's disease (CD) is common after intestinal resection, with over 50% developing endoscopic lesions within a year if untreated. The increasing availability of biologics and small molecules has transformed postoperative management, yet optimal strategies for prevention and treatment remain unclear. Infliximab and vedolizumab have the strongest evidence for preventing endoscopic recurrence in postoperative Crohn's disease. Adalimumab and ustekinumab are viable alternatives supported by observational and post hoc trial data. Selective IL-23 inhibitors and JAK inhibitors have demonstrated high efficacy in moderate to severe luminal CD but lack dedicated postoperative trials. Personalized strategies, such as therapeutic drug monitoring (TDM), model informed dosing and pharmacogenetic profiling hold promise for improving long-term control of postoperative Crohn's disease. Important gaps remain, particularly regarding the drug concentrations to target, the optimal timing for intervention, and the identification of patients most likely to benefit. Approaches that integrate disease location, clinical risk profiles, and knowledge of underlying immunopathogenic pathways could provide more precise clinical guidance. Finding molecular predictors of recurrence, directly comparing cutting-edge treatments, and integrating precision medicine techniques into standard postoperative care should be the main priorities of future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".