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Record W1989295041 · doi:10.1159/000194009

A Single-Breath Method of Alveolar O <sub>2</sub> Determination

2009· article· en· W1989295041 on OpenAlexaff
V. Vu-Dinh Minh, D. Patakas, Lynne Davies, Brian J. Sproule

Bibliographic record

VenueRespiration · 2009
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineRest (music)Significant differenceMean differenceArterial bloodExpired airNuclear medicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

We designed a single-breath method of alveolar O&lt;sub&gt;2&lt;/sub&gt; determination, requiring only a single tidal breath expirate and a concomitant arterial blood sample. The PAO&lt;sub&gt;2&lt;/sub&gt; equation used in our method was derived by applying the Bohr equation to both O&lt;sub&gt;2&lt;/sub&gt; and CO&lt;sub&gt;2&lt;/sub&gt; and independant of VO&lt;sub&gt;2&lt;/sub&gt;, VCO&lt;sub&gt;2&lt;/sub&gt; and RQ. In 35 patients with different degrees of airway obstruction, at rest and during exercise, the single-breath method agreed well with the classic method of PAO&lt;sub&gt;2&lt;/sub&gt; determination which required 3 min of expired gas collection and derivation of VO&lt;sub&gt;2&lt;/sub&gt;, VCO&lt;sub&gt;2&lt;/sub&gt; and RQ. The mean difference between the PAO&lt;sub&gt;2&lt;/sub&gt; estimates by the two methods was 0.36 mm Hg as calculated for all patients, both at rest and during exercise. At rest, in 2 out of 35 cases the difference was greater than 2 mm Hg and such difference happened only in 1 case during exercise. A good correlation existed between the two PAO&lt;sub&gt;2&lt;/sub&gt; estimates by the two methods (r ranging from 0.977 to 0.996). The data indicated that single-breath method of PAO&lt;sub&gt;2&lt;/sub&gt; determination was reliable. Its extreme simplicity would facilitate greatly the assessment of gas exchange efficiency in situations where both patient’s cooperation and laboratory equipment are less than optimum.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.498

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 head0.030
GPT teacher head0.330
Teacher spread0.301 · 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 designBench or experimental
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

Citations0
Published2009
Admission routes1
Has abstractyes

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