Submaximal exercise with self-contained breathing apparatus: the effects of hyperoxia and inspired gas density.
Bibliographic record
Abstract
BACKGROUND: The self-contained breathing apparatus (SCBA) used by firefighters, and other working in dangerous environments, adds an external resistance to expiration, which increases expiratory work during heavy exercise. HYPOTHESIS: Compressed air is typically used with the SCBA and we hypothesized that changing the inspired oxygen concentration and/or gas density with helium would reduce the external expiratory resistance. METHODS: On separate days, 15 men completed four 30-min bouts of treadmill exercise dressed in protective clothing and breathing the test gases through the SCBA. Four different gas mixtures were assigned in random order: [compressed air (NOX: 21% O2, 79% N2), hyperoxia (HOX: 40% O2, 60% N2), normoxic-helium (HE-OX: 21% O2, 79% He), and helium-hyperoxia (HE-HOX: 40% O2, 60% He)]. RESULTS: Compared with NOX, the two helium mixtures (but not HOX), decreased the external breathing resistance and all three gas mixtures decreased the peak expired mask pressure and the ventilatory mass moved. Both hyperoxic mixtures decreased blood lactate and the rating of perceived exertion was decreased at 30 min with HE-HOX. CONCLUSIONS: These results demonstrate that the helium-based gas mixtures, and to a lesser extent HOX, reduce the expiratory work associated with the SCBA during strenuous exercise.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".