Efficiency of oxygen administration: Sequential gas delivery versus “flow into a cone” methods
Bibliographic record
Abstract
OBJECTIVE: Fio2 values of a new oxygen mask that exploits efficiencies afforded by sequential gas delivery (SGD) were compared to those of a nonrebreathing mask (NRM) and a Venturi oxygen mask. DESIGN: Prospective, single-blinded, randomized study. SETTING: Laboratory study. SUBJECTS: Eight healthy male volunteers. INTERVENTIONS: Volunteers breathed through each of the masks at various minute ventilations (VE). Oxygen flows were 2, 4, and 8 L/min to the SGD mask but only 8 L/min to the other masks. MEASUREMENTS AND MAIN RESULTS: Net FIO2 was calculated from end-tidal fractional concentrations of oxygen and CO2 with the alveolar gas equation. Only the SGD mask at an oxygen flow of 8 L/min consistently provided both FIO2>0.95 (at resting VE) and higher FIO2 than the other masks at all VE. The SGD mask delivered FIO2 comparable to other masks at only a fraction of the oxygen flow and was characterized by a consistent relation between FIO2 and oxygen flow for a given VE. CONCLUSION: We conclude that SGD can be exploited to provide FIO2>0.95 with oxygen flows as low as 8 L/min, as well as accurate and efficient dosing of oxygen even in the presence of hyperpnea.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".