Optimal phasic tracheal gas insufflation timing: An experimental and mathematical analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the modulation of CO2 clearance by changes in the duration of tracheal gas flow application during tracheal gas insufflation (TGI). DESIGN: Combination of bench studies using a commercial test lung and a commercially available intensive care ventilator and mathematical analysis using a clearance model derived from first principles. SETTING: University pulmonary research laboratory. PATIENTS: None. INTERVENTIONS: Experiments using TGI were performed on a test lung at two combinations of tidal volume and frequency. TGI was limited to part of the expiratory phase (the terminal 10-100% of expiration), and two different TGI catheter flow rates were studied. Permutations over a range of compliances, dead-space volumes, catheter flows, and TGI durations were collected. A mathematical model incorporating key ventilatory and TGI-related variables was developed to provide a first-principles theoretical foundation for interpreting the experimental results. MEASUREMENTS AND MAIN RESULTS: In the physical model, alveolar Pco2 attained a minimum value with TGI flow applied during the terminal 40-60% of the expiratory phase, a finding that was consistent over an almost eight-fold range of expiratory time constants. The mathematical model shows the same qualitative pattern as the experimental model, indicating that the observed behaviors are not an experimental artifact. CONCLUSION: The optimal duration of expiratory TGI flow application is stable over a wide range of impedance characteristics. Such stability suggests that near maximal effect of expiratory TGI could be obtained by applying TGI flow solely within the final 50% of the expiratory phase. Such uniform restriction of the application profile might both simplify technique implementation and decrease adverse consequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".