Development of an Objective Model to Define Near-Term Risk of Ileocecal Resection in Patients with Terminal Ileal Crohn Disease
Bibliographic record
Abstract
BACKGROUND: The decision to either escalate medical therapy or proceed to ileocecal resection (ICR) in patients with terminal ileal Crohn disease (CD) remains largely subjective. We sought to develop a risk score for predicting ICR at 1 year from computed tomography or magnetic resonance enterography (CTE/MRE). METHODS: We conducted a retrospective cohort study including all consecutive adult (> 18 years) patients with imaging findings of terminal ileal CD (Montreal classification: B1, inflammatory predominant; B2, stricturing; or B3, penetrating) on CTE/MRE between January 1, 2016, and December 31, 2016. The risk for ICR at 6 months and at 1 year of CTE/MRE and risk factors associated with ICR, including demographics, CD-specific immunosuppressive therapeutics, and disease presentation at the time of imaging, were determined. RESULTS: Of 559 patients, 121 (21.6%) underwent ICR during follow-up (1.4 years [IQR 0.21-1.64 years]); the risk for ICR at 6 months and at 1 year was 18.2% (95% CI 14.7%-21.6%) and 20.5% (95% CI 16.8%-24.1%), respectively. Multivariable analysis revealed Montreal classification (B2, hazard ratio [HR] 2.73, and B3, HR 6.80, both P < 0.0001), upstream bowel dilation (HR 3.06, P < 0.0001), and younger age (19-29 years reference, 30-44 years, HR 0.83 [P = 0.40]; 45-59 years, HR 0.58 [P = 0.04], and 60+ years, HR 0.45 [P = 0.01]) to significantly increase the likelihood of ICR. A predictive nomogram for interval ICR was developed based on these significant variables. CONCLUSIONS: The presence of CD strictures, penetrating complications, and upstream bowel dilation on CTE/MRE, combined with young age, significantly predict ICR. The suggested risk model may facilitate objective therapeutic decision-making.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".