Prospective targeting and control of end‐tidal CO<sub>2</sub> and O<sub>2</sub> concentrations
Bibliographic record
Abstract
Current methods of forcing end-tidal PCO2 (PETCO2) and PO2 (PETO2) rely on breath-by-breath adjustment of inspired gas concentrations using feedback loop algorithms. Such servo-control mechanisms are complex because they have to anticipate and compensate for the respiratory response to a given inspiratory gas concentration on a breath-by-breath basis. In this paper, we introduce a low gas flow method to prospectively target and control PETCO2 and PETO2 independent of each other and of minute ventilation in spontaneously breathing humans. We used the method to change PETCO2 from control (40 mmHg for PETCO2 and 100 mmHg for PETO2) to two target PETCO2 values (45 and 50 mmHg) at iso-oxia (100 mmHg), PETO2 to two target values (200 and 300 mmHg) at normocapnia (40 mmHg), and PETCO2 with PETO2 simultaneously to the same targets (45 with 200 mmHg and 50 with 300 mmHg). After each targeted value, PETCO2 and PETO2 were returned to control values. Each state was maintained for 30 s. The average difference between target and measured values for PETCO2 was +/-1 mmHg, and for PETO2 was +/-4 mmHg. PETCO2 varied by +/-1 mmHg and PETO2 by +/-5.6 mmHg (s.d.) over the 30 s stages. This degree of control was obtained despite considerable variability in minute ventilation between subjects (+/-7.6 l min(-1)). We conclude that targeted end-tidal gas concentrations can be attained in spontaneously breathing subjects using this prospective, feed-forward, low gas flow system.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".