Feasibility of neurally synchronized and proportional negative pressure ventilation in a small animal model
Bibliographic record
Abstract
Rationale Synchronized positive pressure ventilation is possible using diaphragm electrical activity (EAdi) to control the ventilator. It is unknown whether EAdi can be used to control negative pressure ventilation. Aim To evaluate the feasibility of using EAdi to control negative pressure ventilation. Methods Fourteen anesthetized rats were studied (380–590 g) during control, resistive breathing, acute lung injury or CO2 rebreathing. Positive pressure continuous neurally adjusted ventilatory assist (cNAVAP+) was applied via intubation. Negative pressure cNAVA (cNAVAP−) was applied with the animal placed in a sealed box. In part 1, automatic stepwise increments in cNAVA level by 0.2 cmH2O/µV every 30 s was applied for cNAVAP+, cNAVAP−, and a 50/50 combination of the two (cNAVAP±). In part 2: During 5-min ventilation with cNAVAP+ or cNAVAP− we measured circuit, box, and esophageal (Pes) pressure, EAdi, blood pressure, and arterial blood gases. Results Part 1: During cNAVAP+, pressure in the circuit increased with increasing cNAVA levels, reaching a plateau, and similarly for cNAVAP−, albeit reversed in sign. This was associated with downregulation of the EAdi. Pes swings became less negative with cNAVAP+ but, in contrast, Pes swings were more negative during increasing cNAVAP− levels. Increasing the cNAVA level during cNAVAP± resulted in an intermediate response. Part 2: no significant differences were observed for box/circuit pressures, EAdi, blood pressure, or arterial blood gases. Pes swings during cNAVAP− were significantly more negative than during cNAVAP+. Conclusion Negative pressure ventilation synchronized and proportional to the diaphragm activity is feasible in small animals.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".