Integration of CO2 clearance and continuous neurally adjusted ventilatory assist in an animal model of respiratory distress
Bibliographic record
Abstract
Background A mode of ventilation is introduced that integrates CO 2 clearance (“Through-Flow”, TF) with Continuous Neurally Adjusted Ventiator Assist (cNAVA). The new mode, referred to as “TF+cNAVA”, is a unidirectional flow system where inspiratory lumen(s) within an ET tube delivers fresh gas at the carina and the main ET tube lumen allows overflow of fresh gas to provide assist in proportion to the electrical activity of the diaphragm (Edi). The aim was to evaluate the effect of TF+cNAVA on breathing parameters during respiratory provocations, its reproducibility, and its sustainability. Conventional NAVA was the comparison. Methods 46 rabbits were studied in three protocols: (i) NAVA vs. TF+cNAVA in open chest with added dead space, acute lung injury (ALI), single lung ventilation, and vagotomy (n = 8); (ii) Eight repeated comparisons, to demonstrate reproducibility (n = 8); (iii) ALI rabbits (n=30) randomized to either NAVA or TF+cNAVA (6 hours). Parameters studied: Edi, tidal volume (V T ), breathing frequency (F B ), minute ventilation (V E ), transpulmonary pressure (P L ), and PaCO 2 . Results With added provocations, NAVA showed an increase in Edi, V T , F B , P L and V E from baseline by 43% (P=0.004). TF+cNAVA significantly reduced Edi, V T , F B , P L , and V E from baseline, by 73% (P<0.001). In NAVA vs. TF+cNAVA crossover, TF+cNAVA consistently reached apneic levels for Edi, V T , F B , P L , and V E . During 6 hours of TF+cNAVA, PaCO 2 , Edi, V T , F B , P L , and V E were significantly reduced compared to NAVA and baseline. Conclusion TF+cNAVA unloads respiratory muscles and suppresses respiratory drive, while reducing lung distending pressure.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".