Temporary transvenous diaphragmatic neurostimulatiom distributes tidal volume in a more physiological pattern versus mechanical ventilation alone.
Bibliographic record
Abstract
Mechanical ventilation in sedated patients overinflates caudal alveoli and underinflates rostral alveoli, causing volutrauma and atelectrauma thus resulting in ventilator induced lung injury. Temporary transvenous diaphragmatic neurostimulatiom (TTDN) stimulates diaphragm contraction. When used in synchrony with ventilation, TTDN yields a more normal physiological breathing pattern by promoting homogenous distribution of ventilation, improving gas exchange and reducing injury. A pilot study was conducted using 50 kg pigs ventilated in a mock ICU. Lung-protective volume control ventilation at 8 ml/kg was provided under deep sedation. TTDN therapy was delivered in synchrony with inspiration on every second breath to reduce the ventilator pressure-time-product by 15-20% for the TTDN+MV group. This was compared to a mechanical ventilation only group (MV) and a never ventilated, spontaneous breathing group (NV). Alveolar chord length, estimating the mean free distance between gas exchange surfaces, was measured from histology samples for each group. TTDN therapy used as an adjunct to ventilation yields a more physiological pattern of alveoli expansion. This translates into less overdistension in the ventral areas and less atelectrauma in the dorsal areas and reduces lung injury. This technology has the potential to provide a novel method of lung-protective ventilation.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".