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Record W2161240918 · doi:10.1093/jat/33.8.514

Examination of Some Performance Characteristics of Breath Alcohol Measurements Obtained with the Intoxilyzer(R) 8000C Following Social Drinking Conditions

2009· article· en· W2161240918 on OpenAlexaff
James H. Watterson, Kayla N. Ellefsen

Bibliographic record

VenueJournal of Analytical Toxicology · 2009
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsLaurentian University
Fundersnot available
KeywordsAlcoholBlood alcoholBreath testAnimal scienceVenous bloodLimits of agreementPoison controlMedicineChemistryInjury preventionNuclear medicineInternal medicineBiologyEnvironmental healthBiochemistry

Abstract

fetched live from OpenAlex

The Intoxilyzer 8000C was used to measure breath alcohol concentration (BrAC) in 10 healthy subjects under social drinking conditions. Measurements commenced within 5 min of the end of drinking (EOD). For 14 blood-breath pairs, measured BrACs were compared to corresponding venous blood alcohol concentrations (vBAC) of samples drawn at least 30 min after EOD and within 5 min of the corresponding breath test. BAC was analyzed using an enzymatic method. Concentration differences between breath and blood (BrAC - vBAC) ranged from -32 to +3 mg/dL (untruncated BrAC) and from -32 to -4 mg/dL (truncated BrAC). The Invalid Sample message was actuated in five out of 23 BrAC profiles. In the remaining 18 samples, residual mouth alcohol was evaluated by comparing the maximum difference between successive (5 min apart) measurements (MID5) over 20-30 min after EOD with the precision of replicate BrAC values taken 30-40 min after EOD (5 mg/dL or less; precision unaffected by breath sample volume over the range of 2-3 L). MID5 values occurred within the first three measurements in 16/18 cases, indicative of a significant mouth alcohol effect. Thus, mandatory delays should be used with the Intoxilyzer 8000C prior to testing to minimize the probability of overestimation of BrAC due to mouth alcohol.

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)0.001
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 score0.498
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.094
GPT teacher head0.380
Teacher spread0.286 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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