Pressure Estimator of Airway Narrowing in Total Liquid Ventilation: First Results in Guinea Pigs
Bibliographic record
Abstract
Total Liquid Ventilation (TLV) consists of ventilating the lungs of mammals with a breathable liquid (PFOB). This technology is being considered for extremely premature baby (less than 1 kg). However, liquid expiration in the high flexible airways can trigger a sudden tracheal collapse, observed as a sudden pressure drop at the endotracheal tube connector. To our knowledge, there is no reported TLV experiments in mammals of very low weight, ranged from 507 to 552 g (n=4), nor has tracheal collapse been observed. The objective is to develop a real-time pressure estimator to monitor the airway opening as well as to control the expiratory flow through a 2 mm diameter endotracheal tube. The experiments were carried out on 4 guinea pigs with a tracheotomy using a liquid ventilator during 4 hours. After 1 hour of TLV, acceptable blood gases were obtained proving that total liquid ventilation was tolerated. The experimental flow and pressure measurements from the first two experiments were used to learn the non-linear dynamics and the model parameters. The model was implemented in real-time on the microcontroller and used to test two control strategies of the expiratory flow. The last two guinea pigs were ventilated with the implemented estimator to detect sudden airway narrowing and to automatically control the expiratory flow rate. The results confirmed the effectiveness of the method in reversing the sudden pressure drop. In conclusion, the pressure estimator can be helpful in controlling liquid ventilation in mammals of very low weight, comparable to extreme premature babies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".