Diaphragm Neurostimulation Assisted Ventilation in Critically Ill Patients
Bibliographic record
Abstract
Diaphragm neurostimulation consists of placing electrodes directly on or in proximity to the phrenic nerve(s) to elicit diaphragmatic contractions. Since its initial description in the 18th century, indications have shifted from cardiopulmonary resuscitation to long-term ventilatory support. Recently, the technical development of devices for temporary diaphragm neurostimulation has opened up the possibility of a new era for the management of mechanically ventilated patients. Combining positive pressure ventilation with diaphragm neurostimulation offers a potentially promising new approach to the delivery of mechanical ventilation which may benefit multiple organ systems. Maintaining diaphragm contractions during ventilation may attenuate diaphragm atrophy and accelerate weaning from mechanical ventilation. Preventing atelectasis and preserving lung volume can reduce lung stress and strain and improve homogeneity of ventilation, potentially mitigating ventilator-induced lung injury. Furthermore, restoring the thoracoabdominal pressure gradient generated by diaphragm contractions may attenuate the drop in cardiac output induced by positive pressure ventilation. Experimental evidence suggests diaphragm neurostimulation may prevent neuroinflammation associated with mechanical ventilation. This review describes the historical development and evolving approaches to diaphragm neurostimulation during mechanical ventilation and surveys the potential mechanisms of benefit. The review proposes a research agenda and offers perspectives for the future of diaphragm neurostimulation assisted mechanical ventilation for critically ill patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".