Non‐invasive prospective targeting of arterial <i>P</i> in subjects at rest
Bibliographic record
Abstract
Accurate measurements of arterial P(CO(2)) (P(a,CO(2))) currently require blood sampling because the end-tidal P(CO(2)) (P(ET,CO(2))) of the expired gas often does not accurately reflect the mean alveolar P(CO(2)) and P(a,CO(2)). Differences between P(ET,CO(2)) and P(a,CO(2)) result from regional inhomogeneities in perfusion and gas exchange. We hypothesized that breathing via a sequential gas delivery circuit would reduce these inhomogeneities sufficiently to allow accurate prediction of P(a,CO(2)) from P(ET,CO(2)). We tested this hypothesis in five healthy middle-aged men by comparing their P(ET,CO(2)) values with P(a,CO(2)) values at various combinations of P(ET,CO(2)) (between 35 and 50 mmHg), P(O(2)) (between 70 and 300 mmHg), and breathing frequencies (f; between 6 and 24 breaths min(-1)). Once each individual was in a steady state, P(a,CO(2)) was collected in duplicate by consecutive blood samples to assess its repeatability. The difference between P(ET,CO(2)) and average P(a,CO(2)) was 0.5 +/- 1.7 mmHg (P = 0.53; 95% CI -2.8, 3.8 mmHg) whereas the mean difference between the two measurements of P(a,CO(2)) was -0.1 +/- 1.6 mmHg (95% CI -3.7, 2.6 mmHg). Repeated measures ANOVAs revealed no significant differences between P(ET,CO(2)) and P(a,CO(2)) over the ranges of P(O(2)), f and target P(ET,CO(2)). We conclude that when breathing via a sequential gas delivery circuit, P(ET,CO(2)) provides as accurate a measurement of P(a,CO(2)) as the actual analysis of arterial blood.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".