Prophylactic Medication for the Prevention of Endoscopic Recurrence in Crohn’s Disease: a Prospective Study Based on Clinical Risk Stratification
Bibliographic record
Abstract
BACKGROUND: To prevent recurrence after ileocolonic resection [ICR] in Crohn's disease [CD], postoperative prophylaxis based on risk stratification is recommended in international guidelines. This study aimed to evaluate postoperative CD recurrence after implementation of a clinical management algorithm and to determine the predictive value of clinical and histological risk factors [RFs]. METHODS: In this multicentre, prospective cohort study, CD patients [≥16 years] scheduled for ICR were included. The algorithm advised no postoperative medication for low-risk patients, and treatment with prophylaxis [immunosuppressant/biological] for high-risk patients [≥1 RF: active smoking, penetrating disease, prior ICR]. Clinical and histological RFs [active inflammation, granulomas, plexitis in resection margins] for endoscopic recurrence [Rutgeerts' score ≥i2b at 6 months] were assessed using logistic regression and ROC curves based on predicted probabilities. RESULTS: In total, 213 CD patients after ICR were included [age 34.5 years; 65% women] (93 [44%] low-risk; 120 [56%] high-risk: 45 [38%] smoking; 51 [43%] penetrating disease; 51 [43%] prior ICR). Adherence to the algorithm was 82% in low-risk [no prophylaxis] and 51% in high-risk patients [prophylaxis]. Endoscopic recurrence was higher in patients treated without prophylaxis than with prophylaxis in both low [45% vs 16%, p = 0.012] and high-risk patients [49% vs 26%, p = 0.019]. Clinical risk stratification including the prescription of prophylaxis corresponded to an area under the curve [AUC] of 0.70 (95% confidence interval [CI] 0.61-0.79). Clinical RFs combined with histological RFs increased the AUC to 0.73 [95% CI 0.64-0.81]. CONCLUSION: Adherence to this management algorithm is 65%. Prophylactic medication after ICR prevents endoscopic recurrence in low- and high-risk patients. Clinical risk stratification has an acceptable predictive value, but further refinement is needed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".