Bibliographic record
Abstract
Breath acetaldehyde has been used to investigate the production of acetaldehyde after ethanol ingestion in ALDH2-deficient individuals, to compare ethanol and acetaldehyde metabolism, to study the toxicological outcome of metabolic inhibitors of ALDH2, and as a biomarker of exposure to ethanol vapours. A number of approaches have been developed to collect representative breath samples (mixed air or alveolar air) and to measure breath acetaldehyde. For instance, the highest breath concentration of acetaldehyde (approximately 50 nmoles/1) measured during pulmonary ethanol exposure (1000ppm, 6 hours) is of the same magnitude as those measured after ingestion of 0.4-0.8 g/kg (-60-80 nmoles/ i), whereas endogenous levels rarely exceed 1 nmole/l. The interpretation of breath acetaldehyde is compounded by several factors; smoking, ALDH2 polymorphism and alcohol drinking habits are associated with higher breath/blood levels. Some authors have considered that breath acetaldehyde, particularly low levels, cannot be used to estimate blood acetaldehyde. Despite the problems associated with its determination, breath acetaldehyde could be an interesting tool for estimating ethanol or acetaldehyde exposure. However, some additional research efforts will be necessary in order to standardize the technique used for breath sampling and to control the influence of the factors that are known to affect breath acetaldehyde determination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".