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Record W2902584907 · doi:10.28920/dhm48.4.209-217

Comparison of tissue oxygenation achieved breathing oxygen from a demand valve with four different mask configurations

2018· article· en· W2902584907 on OpenAlexaff
Denise Blake, Melissa Crowe, Daniel Lindsay, Annie Brouff, Simon J Mitchell, Neal W. Pollock

Bibliographic record

VenueDiving and Hyperbaric Medicine Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsMedicineBreathingOxygenationNoseAnesthesiaSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: High concentration normobaric oxygen (O₂) is a priority in treating divers with suspected decompression illness. The effect of different O₂ mask configurations on tissue oxygenation when breathing with a demand valve was evaluated. METHODS: Sixteen divers had tissue oxygen partial pressure (PtcO₂) measured at six limb sites. Participants breathed O₂ from a demand valve using: an intraoral mask (IOM®) with and without a nose clip (NC), a pocket face mask and an oronasal mask. In-line inspired O₂ (FIO₂) and nasopharyngeal FIO₂ were measured. Participants provided subjective ratings of mask comfort, ease of breathing and holding in position. RESULTS: PtcO₂ values and nasopharyngeal FIO₂ (median and range) were greatest using the IOM with NC and similar with the IOM without NC. O₂ measurements were lowest with the oronasal mask which also was rated as the most difficult to breathe from and to hold in position. The pocket face mask was reported as the most comfortable to wear. The NC was widely described as uncomfortable. The IOM and pocket face mask were rated best for ease of breathing. The IOM was rated as the easiest to hold in position. CONCLUSION: Of the commonly available O₂ masks for use with a demand valve, the IOM with NC achieved the highest PtcO₂ values. PtcO₂ and nasopharyngeal FIO₂ values were similar between the IOM with and without NC. Given the reported discomfort of the NC, the IOM without NC may be the best option.

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 score0.298
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.036
GPT teacher head0.310
Teacher spread0.274 · 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
Published2018
Admission routes1
Has abstractyes

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