Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease
Bibliographic record
Abstract
Strong evidence suggests that the gut microbiota is altered in inflammatory bowel disease (IBD), indicating its potential role in noninvasive diagnostics. However, no clinical applications are currently used for routine patient care. The main obstacle to implementing a gut microbiota test for IBD is the lack of standardization, which leads to high interlaboratory variation. We studied the between-hospital and between-platform batch effects and their effects on predictive accuracy for IBD. Fecal samples from 91 pediatric IBD patients and 58 healthy children were collected. IS-pro, a standardized technique designed for routine microbiota profiling in clinical settings, was used for microbiota composition characterization. Additionally, a large synthetic data set was used to simulate various perturbations and study their effects on the accuracy of different classifiers. Perturbations were validated in two replicate data sets, one processed in another laboratory and the other with a different analysis platform. The type of perturbation determined its effect on predictive accuracy. Real-life perturbations induced by between-platform variation were significantly greater than those caused by between-laboratory variation. Random forest was found to be robust to both simulated and observed perturbations, even when these perturbations had a dramatic effect on other classifiers. It achieved high accuracy both when cross-validated within the same data set and when using data sets analyzed in different laboratories. Robust clinical predictions based on the gut microbiota can be performed even when samples are processed in different hospitals. This study contributes to the effort to develop a universal IBD test that would enable simple diagnostics and disease activity 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 | 0.002 | 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) | 0.001 | 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".