Timing is Everything: Lessons Learned for Building Microbiome-Based Models in Pediatric Crohn’s Disease
Bibliographic record
Abstract
Crohn’s disease (CD) pathophysiology remains not fully understood but is hypothesized to result from a complex interplay between genetic and environmental factors that seem to manifest through the microbiome.1 Recognition that microbiome dysbiosis is a key mechanism in inflammatory bowel disease (IBD) pathophysiology has led to the development of various microbiome-based models with the aim of developing diagnostic biomarkers in both children and adults.1,2 Application of microbiome-based models completely relies on patient selection, microbiome sampling, and the (type of) technical and statistical analysis. Careful patient (or population) selection is crucial in microbiome research, as the microbiome is significantly influenced by various environmental factors.3 Gut microbial samples can be acquired via mucosal biopsies, rectal swabs, or fecal samples collection. Previous research hdeas advocated in favor of mucosal samples, as they are hypothesized to most accurately represent the “local” microbiome.4 Nevertheless, fecal samples remain the most common and non-invasive method of collection.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".