Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The microbiome contributes to the pathogenesis of inflammatory bowel disease (IBD) but the relative contribution of different lifestyle and environmental factors to the compositional variability of the gut microbiota is unclear. DESIGN: Here, we rank the size effect of disease activity, medications, diet and geographic location of the faecal microbiota composition (16S rRNA gene sequencing) in patients with Crohn's disease (CD; n=303), ulcerative colitis (UC; n = 228) and controls (n=161), followed longitudinally (at three time points with 16 weeks intervals). RESULTS: Reduced microbiota diversity but increased variability was confirmed in CD and UC compared with controls. Significant compositional differences between diseases, particularly CD, and controls were evident. Longitudinal analyses revealed reduced temporal microbiota stability in IBD, particularly in patients with changes in disease activity. Machine learning separated disease from controls, and active from inactive disease, when consecutive time points were modelled. Geographic location accounted for most of the microbiota variance, second to the presence or absence of CD, followed by history of surgical resection, alcohol consumption and UC diagnosis, medications and diet with most (90.3%) of the compositional variance stochastic or unexplained. CONCLUSION: The popular concept of precision medicine and rational design of any therapeutic manipulation of the microbiota will have to contend not only with the heterogeneity of the host response, but also with widely differing lifestyles and with much variance still unaccounted for.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it