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Record W3104178433 · doi:10.1038/s41598-020-76233-9

Accelerated ethanol elimination via the lungs

2020· article· en· W3104178433 on OpenAlexaff
Jesse Klostranec, Diana Vucevic, Adrian P. Crawley, Lashmi Venkatraghavan, Olivia Sobczyk, James Duffin, Kevin Sam, Royce Holmes, Ludwik Fedorko, David J. Mikulis, Joseph A. Fisher

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of TorontoUniversity Health NetworkThornhill Medical (Canada)Toronto Western HospitalMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsEthanolClearance rateChemistryRespiratory systemRespirationToxicokineticsMetabolismAnesthesiaMedicineInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Ethanol poisoning is endemic the world over. Morbidity and mortality depend on blood ethanol levels which in turn depend on the balance between its rates of absorption and clearance. Clearance of ethanol is mostly at a constant rate via enzymatic metabolism. We hypothesized that isocapnic hyperpnea (IH), previously shown to be effective in acceleration of clearance of vapour anesthetics and carbon monoxide, would also accelerate the clearance of ethanol. In this proof-of-concept pilot study, five healthy male subjects were brought to a mildly elevated blood ethanol concentration (~ 0.1%) and ethanol clearance monitored during normal ventilation and IH on different days. IH increased elimination rate of ethanol in proportion to blood levels, increasing the elimination rate more than three-fold. Increased veno-arterial ethanol concentration differences during IH verified the efficacy of ethanol clearance via the lung. These data indicate that IH is a nonpharmacologic means to accelerate the elimination of ethanol by superimposing first order elimination kinetics on underlying zero order liver metabolism. Such kinetics may prove useful in treating acute severe ethanol intoxication.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.128
GPT teacher head0.375
Teacher spread0.247 · 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 designNot applicable
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

Citations5
Published2020
Admission routes1
Has abstractyes

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