Untangling the nets: a roadmap to standardized sampling and analysis of exhaled volatile organic compounds powered by in silico medicine
Bibliographic record
Abstract
Biomarkers based on volatile organic compounds (VOCs) measured in human breath have been investigated in a wide range of diseases. However, the excitement surrounding such biomarkers has not yet translated to the discovery of any that are ready for clinical implementation. A lack of standardization in sampling and analysis has been identified as a key obstacle to the validation of potential biomarkers in multicenter studies. Some progress toward standardization has been made, but further progress is required to optimize sampling protocols and account for the confounding factors identified. This review highlights the important role that in silico (i.e., computational modeling) methods can play in addressing these gaps. Moreover, we discuss their potential for targeting and validating disease biomarkers by mechanistically linking them to the underlying metabolomic processes. We explore pertinent examples of mathematical, computational, and machine learning models that have proven useful in similar contexts, such as the development of fractional exhaled nitric oxide sampling standards. We then propose a roadmap outlining how existing and new modeling approaches can be applied to the problem of standardization in breathomics research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".