S21 APEX Score Prospective Validation: An Excellent Predictor of Flares in Small Bowel Crohn’s Disease After Mucosal Healing
Bibliographic record
Abstract
Background: Optimal strategies for monitoring isolated small bowel’s Crohn’s Disease (CD) remain uncertain. In 2020, our group created the APEX score, with a value between 4 and 7 presenting an excellent accuracy at stratifying patients relapse risk following a small bowel capsule endoscopy (SBCE) showing mucosal healing (MH). Our aim was to prospectively validate APEX score accuracy in predicting disease flares on the year following SBCE. Methods: Our study prospectively included patients with isolated small bowel CD (Montreal L1±L4) undergoing SBCE, who were in clinical remission (CDAI< 150) and corticosteroid-free for the previous 6 months. A blood sample was collected within a maximum of 15 days of SBCE. The score APEX (Age ≤30 years +3/Platelets ≥ 280 × 103/L +2/Extraintestinal manifestations +2) was applied on patients whose SBCE reported MH. Disease flares were documented on the subsequent year. Results: We have included 47 patients, from which 28 (59.6%) presented MH on SBCE. From these, 21 (75%) were female, with a mean age of 42±13 years. On the following year, a disease flare was documented on 4 (14.3%) patients. A high-risk APEX score was found in 5 (17.9%) of the patients. The APEX score presented an excellent accuracy in predicting disease flares on the year following MH (AUC=0.97; 95%CI 0.92-1.00; p=0.003). The previously calculated optimal cut-off (APEX≥4) had a sensitivity of 100% and a specificity of 95.8% in predicting the outcome. Conclusions: Patients with small bowel CD and MH still have a non-neglectable risk of disease flare on the subsequent year. The APEX score has prospectively demonstrated excellent accuracy at stratifying patients’ relapse risk. Thus, it emerges as a helpful tool in patients with MH, by identifying those who will need earlier evaluations and more frequent monitoring.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".