Neurally Adjusted Ventilatory Assist and Pressure Support Ventilation in Small Species and the Impact of Instrumental Dead Space
Bibliographic record
Abstract
BACKGROUND: Neurally adjusted ventilatory assist (NAVA) is a pneumatically-independent mode of mechanical ventilation controlled by diaphragm electrical activity (EAdi), and has not yet been implemented in very small species. OBJECTIVES: The aims of the study were to evaluate the feasibility of applying NAVA in very small species and to compare this to pressure support ventilation (PSV) in terms of ventilatory efficiency and breathing pattern, and evaluate the impact of instrumental dead space on breathing pattern during both modes. METHODS: Nine healthy rats (mean weight 385 +/- 4 g) were studied while breathing on PSV or NAVA, at baseline or with added dead space. RESULTS: A clear difference in breathing pattern between NAVA and PSV was observed during both baseline and dead space, where PSV - despite similar EAdi and tidal volume as during NAVA - caused shortened inspiratory time (p < 0.05) and increased the respiratory rate (p < 0.05). A higher minute ventilation (p < 0.05) in order to reach the same arterial CO(2) was observed. Ineffective inspiratory efforts occurred only during PSV and decreased with the dead space. CONCLUSION: This study demonstrates, in a small group of animals, that NAVA can deliver assist in very small species with a higher efficiency than PSV in terms of eliminating CO(2) for a given minute ventilation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".