Lumped Thermal Model of a Newborn Lamb and a Liquid Ventilator in Total Liquid Ventilation
Bibliographic record
Abstract
Total liquid ventilation (TLV) is an emerging and promising mechanical ventilation method in which the lungs are filled with a breathable liquid. Perfluorocarbon (PFC) is the predominant liquid of choice due to its high O2 and CO2 solubility. In TLV, a dedicated liquid ventilator ensures gas exchange by renewing a tidal volume of PFC, which is temperature-controlled, oxygenated and free of CO2. A fundamental difference between TLV and conventional mechanical ventilation relates to the fact that PFCs are approximately 1500 times denser than air. This high density provides PFCs with a large heat capacity, turning the lungs into an efficient heat exchanger with circulating blood. The originality of this study is the development of a lumped thermal model of the body as a heat exchanger coupled to a liquid ventilator. The model was validated with an animal experimentation on a newborn lamb with the Inolivent-5.0 liquid ventilator prototype. TLV was initiated with a fast hypothermia induction, followed successively by a slow posthypothermic rewarming, a fast rewarming and finally a second fast hypothermia induction. Results demonstrate that the model was able to aptly predict, in every phase, the temperature of the lungs, the eardrum, the rectum as well as the various compartments of the liquid ventilator.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".