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Record W1579668559 · doi:10.1002/9780470511848.ch9

The Determination of Acetaldehyde in Exhaled Breath

2006· article· en· W1579668559 on OpenAlexaff
Robert Tardif

Bibliographic record

VenueNovartis Foundation symposium · 2006
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAcetaldehydeBreath gas analysisEthanolChemistryIngestionBreath testAlcoholBiomarkerExpired airChromatographyBiochemistryInternal medicineMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.349
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueNovartis Foundation symposiumSame topicAlcohol Consumption and Health EffectsFrench-language works237,207