Precise Control of End-tidal Carbon Dioxide Levels Using Sequential Rebreathing Circuits
Bibliographic record
Abstract
Anaesthesiologists have traditionally been consulted to help design breathing circuits to attain and maintain target end-tidal carbon dioxide (P(ET)CO2). The methodology has recently been simplified by breathing circuits that sequentially deliver fresh gas (not containing carbon dioxide (CO2)) and reserve gas (containing CO2). Our aim was to determine the roles of fresh gas flow, reserve gas PCO2 and minute ventilation in the determination of P(ET)CO2. We first used a computer model of a non-rebreathing sequential breathing circuit to determine these relationships. We then tested our model by monitoring P(ET)CO2 in human volunteers who increased their minute ventilation from resting to five times resting levels. The optimal settings to maintain P(ET)CO2 independently of minute ventilation are 1) fresh gas flow equal to minute ventilation minus anatomical deadspace ventilation, and 2) reserve gas PCO2 equal to alveolar PCO2. We provide an equation to assist in identifying gas settings to attain a target PCO2. The ability to precisely attain and maintain a target PCO2 (isocapnia) using a sequential gas delivery circuit has multiple therapeutic and scientific applications.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".