Optimal Expiratory Volume Profile in Tidal Liquid Ventilation under Steady State Conditions, Based on a Symmetrical Lung Model
Bibliographic record
Abstract
Liquid-assisted ventilation (LAV) using perfluorochemicals (PFC) offers clear theoretical advantages over gas ventilation. During tidal liquid ventilation (TLV) the residual capacity of the lungs is filled with PFC and a liquid ventilator is necessary to inhale and exhale the tidal volume of PFC. However, during the expiration phase, a flow limitation (choked flow) can be observed, which compromises minute ventilation and consequently the gas exchange. The hypothesis of the presented works is that the choked flow can be avoided by profiling the expiratory volume. To validate this concept, an elastic symmetrical lung numerical model, used to characterize forced expiration in gas ventilation, was transposed to TLV. The parameters of the developed numerical model were fitted from experimental data obtained on a newborn lamb. The results obtained demonstrate that general observations made with gas ventilation still hold, however, in TLV: flow limitation in the central airways is the result of a coupling between viscous pressure losses and airway compliance, and the flow limiting segment is located in the central airways. Using the model results, an optimal theoretical expiratory profile seems to be exponential as first approximation, and its time constant is dependent on the chocked flow mechanism and not on the product of resistance by compliance. This optimal profile is used to compute the maximal minute ventilation allowable with an acceptable risk of collapse. Also, the sensitivity of minute ventilation to different parameter variations were analyzed and practical recommendations are proposed.
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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 | 0.001 |
| 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 | 0.001 | 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".