Monitoring-Based Model for Personalizing Fecal Incontinence in Patients With Crohn’s Disease: A Multicenter Inception Cohort Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Fecal incontinence (FI) is a common complaint that greatly affects the quality of life of patients with Crohn's disease (CD) and is associated with the clinical characteristics of CD. We aimed to identify risk factors related to FI and construct a risk prediction model for FI in patients with CD. METHODS: This retrospective study included 600 Chinese patients with CD from 4 IBD centers between June 2016 and October 2021. The patients were assigned to the training (n = 480) and testing cohorts (n = 120). Two nomograms were developed based on the logistic regression and Cox regression models to predict the risk factors for FI in patients with CD. The discriminatory ability and accuracy of the nomograms were evaluated using the receiver operating characteristic (ROC) curves and the area under the ROC curves (AUCs). Additionally, the Kaplan-Meier survival curve was also used further to validate the clinical efficacy of the Cox regression model. RESULTS: The overall prevalence of FI was 22.3% (n = 134 of 600). In the logistic regression model, age at diagnosis (odds ratio [OR], 1.032; P = .033), penetrating behavior of disease (OR, 3.529; P = .008) and Perianal Disease Activity Index score >4 (OR, 3.068; P < .001) were independent risk factors for FI. In the Cox regression model, age at diagnosis (hazard ratio [HR], 1.027; P = .018), Montreal P classification (HR, 2.608; P = .011), and Perianal Disease Activity Index score >4 (HR, 2.190; P = .001) were independent predictors of the prevalence of FI over time. Two nomograms were developed to facilitate risk score calculation, and they showed good discrimination ability according to AUCs. CONCLUSIONS: In this study, we identified 4 risk factors related to the prevalence of FI and developed 2 models to effectively predict the risk scores of FI in CD patients, helping to delay the course of FI and improve the prognosis with timely intervention.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".