Effective Inspired Oxygen Concentration Measured Via Transtracheal and Oral Gas Analysis
Bibliographic record
Abstract
BACKGROUND: The fraction of inspired oxygen (F(IO(2))) is quoted for different oxygen delivery systems, but variations in inspiratory flow and tidal volume make precise measurement difficult. We developed a reliable method of measuring the effective F(IO(2)) in patients receiving supplemental oxygen. METHODS: Ten subjects with chronic hypoxemia breathed through a mouthpiece with a sampling probe connected to a mass spectrometer. Four of the 10 subjects had transtracheal catheters that allowed direct sampling of tracheal gas. We used oxygen concentrations of 47% and 97%, and flow rates between 1 L/min and 8 L/min. We also compared oxygen delivery via nasal cannula and transtracheal catheter. Effective F(IO(2)) was derived from plots of the fractional concentrations of carbon dioxide versus oxygen. RESULTS: We found excellent correlation between the effective F(IO(2)) values from tracheal and oral sampling (r = 0.960, P < .001). With 97% oxygen via nasal cannula, effective F(IO(2)) increased by 2.5% per liter of increased flow (P < .001); effective F(IO(2)) reached 32.7% at 5 L/min while P(aO(2)) increased by 12 mm Hg per liter of increased flow. In 4 subjects with a transtracheal catheter, effective F(IO(2)) increased 5.0% (P < .001) per liter of increased flow, and P(aO(2)) increased by 13 mm Hg per liter of increased flow, whereas in the same 4 subjects using nasal cannula for oxygen delivery, P(aO(2)) increased by only 6 mm Hg per liter of increased flow. CONCLUSIONS: Exhaled gas sampled at the mouth accurately reflected the effective F(IO(2)) in the trachea. In relation to inspired oxygen flow, the effective F(IO(2)) was lower than is conventionally thought. Compared to nasal cannula, transtracheal catheter approximately doubled the effective F(IO(2)) at a given flow rate. Accurate knowledge of F(IO(2)) should aid clinicians in managing patients with acute and chronic lung diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".