Comparison of tissue oxygenation achieved breathing oxygen from a demand valve with four different mask configurations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".