Bibliographic record
Abstract
Abstract Background Some conditions - such as obesity, depression and functional odor disorders - come with a social stigma. Understanding the etiology of these conditions helps to avoid stereotypes and find remedies. One of the major obstacles facing researchers, especially for those studying socially distressing metabolic malodor, is the difficulty in assembling biologically homogenous study cohorts. Objective The aim of this study was to examine phenotypic variance, self-reported data and laboratory tests for the purpose of identifying clinically relevant and etiologically meaningful subtypes of idiopathic body odor and the “People are Allergic To Me” (PATM) syndrome. Methods Participants with undiagnosed body odor conditions enrolled to participate in this research study initiated by a healthcare charity MEBO Research and sponsored by Wishart Research group at the Metabolomics Innovation Centre, University of Alberta, Canada. Primary outcomes were differences in metabolite concentrations measured in urine, blood and breath of test and control groups. Principal component analyses and other statistical tests were carried out for these measurements. Results While neither of existing laboratory tests could reliably predict chronic malodor symptoms, several measurements distinguished phenotypes at a significance level less than 5%. Types of malodor can be differentiated by self-reported consumption of (or sensitivity to) added sugars (p<0.01), blood alcohols after glucose challenge (especially ethanol: p<0.0005), urinary excretion of phenylalanine, putrescine, and combinations of blood or urine metabolites. Conclusions Our preliminary results suggest that malodor heterogeneity can be addressed by analyses of phenotypes based on patients’ dietary and olfactory observations. Our studies highlight the need for more trials. Future research focused on comprehensive metabolomics and microbiome sequencing will play an important role in the diagnosis and treatment of malodor. Trial Registration The study discussed in the manuscript was registered as NCT02692495 at clinicaltrials.gov. The results were compared with our earlier study registered as NCT02683876 .
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".