Objectifying the Crohnʼs Disease Activity Index (CDAI): Can We Teach an Old Score New Tricks?: Presidential Poster
Bibliographic record
Abstract
Purpose: The CDAI has known limitations, but is still a widely used and accepted instrument in the conduct of clinical trials. Advances in disease assessment and monitoring have led to the development of non-invasive, objective biomarkers of inflammatory disease activity. The CDAI's utility for accurately measuring inflammatory disease burden in CD is questionable. The objective of this study was to compare the predictive ability of patient-reported outcome (PRO)-based and biomarker-based models for endoscopic inflammatory disease activity in subjects with CD. Methods: Between 2004 and 2006, 164 adults with established CD undergoing clinically indicated ileocolonoscopy were recruited to participate in this study. Demographic (gender, age, smoking status, BMI), disease-related (CDAI, number of resections), endoscopic (SES-CD), and biomarker (fecal calprotectin [FC[, IL-6, hsCRP) variables were collected. Endoscopists were blinded to biomarker and CDAI values. The SES-CD was the independent variable against which CDAI was compared. Eight variables from the original CDAI, along with 6 new variables (BMI, smoking status, surgical resection, FC, IL-6, hsCRP) were selected to explore their influence on predictive accuracy for endoscopic disease activity. Simple Poisson regression was performed on each of the variables individually. A robust multivariate model was built using a cross-validated bootstrapping approach. Results: In univariate pre-screening, antidiarrheal/opiate use, smoking status, and presence of resection correlated weakly with the SES-CD score. Abdominal mass, IL-6, and standardized weight were dropped from the model. The remaining variables were added to a bootstrapaggregating (bagging) Poisson regression model. Five variables significantly and consistently related to SES-CD in a multivariate Poisson regression model: number of liquid or soft stools, sum of 7-day abdominal pain ratings, hematocrit (Hct), FC, and hsCRP. For the prediction of SES-CD ≥3 versus ≤ 2, the area under the curve (AUC) was 0.552 (95% CI: [0.543, 0.562]), with sensitivity and specificity of 65.2% and 51.8% for CDAI, compared to 0.682 AUC (95% CI: [0.673, 0.691]) with sensitivity and specificity of 51.1% and 82.9% for the new model, and 0.598 AUC (95% CI: [0.590, 0.608]) with sensitivity and specificity values of 34.2% and 86.4% for a PRO-exclusive model. Conclusion: The CDAI correlates poorly with SES-CD. Incorporation of inflammatory biomarkers into models can improve predictive accuracy for endoscopically active disease. PRO-exclusive models remain inferior to biomarker-based models. Model adaptation to account for variability in SES-CD subcomponents related to inter-subject variability will be explored in the future.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".