Application of metabolomics to the study of irritable bowel syndrome
Bibliographic record
Abstract
The pathophysiology of irritable bowel syndrome and the detection of biomarkers of specific mechanisms and/or predictors of therapeutic response remain elusive. This roadblock reflects, in large part, the complexity and heterogeneity of the disorder. Recently, there has been growing evidence of a dietary and/or microbiome interaction with the host that may trigger symptoms in a subset of patients. While a number of techniques are available to examine these potential interactions, "omic" approaches such as metabolomics are becoming more widely used. Metabolomics measures hundreds and potentially thousands of known and unknown small molecule chemicals (metabolites) to provide a unique look into mechanisms that underlie symptom generation and potential predictors of therapeutic response. In this issue of the journal, Lee et al use nuclear magnetic resonance (NMR) to demonstrate the value of this approach to study IBS. This review examines the use of metabolomics to better understand IBS, focusing on what has been learned to date, practical and technical considerations, its potential for future research and how the study by Lee et al have contributed to these concepts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.003 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".