Regulation of perflubron volume in lung during total liquid ventilation based on the detection of pressure drops
Bibliographic record
Abstract
Each year, around 400 Québec births are considered as extreme preterm. Total liquid ventilation (TLV), using perflubron, may be an alternative to the conventional ventilation, too often insufficient and/or harmful, for some of the most extreme preterm. Inolivent research group develops a TLV technology which controls insufflation and drainage of the breathable liquid in newborn flexible airways. The forced expiratory flow may provoke a pressure drop measured at the endotracheal tube. It is assumed to be occasioned by an upstream flow limitation, a chocked flow in the airways generating a collapsus. To prevent it, end expiratory liquid volume (EELqV) must remain greater than a critical threshold. Conversely, an excess in EELqV might create a deleterious overdistension of the lungs. The main goal was to minimize the EELqV while keeping it above the critical value triggering the tracheal collapse. The method was based on: 1/ The real-time detection of airways collapsus thanks to a dynamic model of pressure estimation, compared with measured pressure. In the absence of collapsus, the model error must be located between +/- 5 cmH2O. A pressure error lower than a detection threshold (-5 cm.H20) implies a correction. 2/ Inspired volume of breathable liquid is based on systematic research of a pressure drop error model, in emptying progressively the lung with a drift of -0.4 mL/kg/cycle. Once the algorithm detects a pressure drop beyond half of the expiratory time, insufflated perflubron volume is increased by 0.9 mL/kg at the next cycle. The strategy was implemented on the Inolivent-10 prototype and experienced on a 2.3 kg piglet under general anesthesia, during 1h13, with CT image capture at the beginning and end of sequence. These latter have been segmented and analyzed with 3D Slicer and allowed to harvest the lung volume. On 306 cycles, 92 include a correction. One may observe a trend of 5 cycles of progressive decrease, removing 2.2 mL/kg of volume in the lungs, followed by 2 cycles with correction, adding 1.3 mL/kg. At the end, the lung volume is about 47.8 mL/kg against 39.1 mL/kg at the beginning, so an increase of 8.7 mL/kg. This first experiment demonstrates the feasibility of automatically seeking and regulating the EELqV during TLV. Upcoming works will must consider machine learning to improve the detection performances lung volume regulation. This control represents a new step for long-term TLV in intensive care units.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".