Prospective targeting and control of end‐tidal CO<sub>2</sub> and O<sub>2</sub> concentrations
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it