Electrical Impedance Tomography to Monitor Hypoxemic Respiratory Failure
Bibliographic record
Abstract
Hypoxemic respiratory failure is one of the leading causes of mortality in intensive care. Frequent assessment of individual physiological characteristics and delivery of personalized mechanical ventilation (MV) settings is a constant challenge for clinicians caring for these patients. Electrical impedance tomography (EIT) is a radiation-free bedside monitoring device that is able to assess regional lung ventilation and changes in aeration. With real-time tomographic functional images of the lungs obtained through a thoracic belt, clinicians can visualize and estimate the distribution of ventilation at different ventilation settings or following procedures such as prone positioning. Several studies have evaluated the performance of EIT to monitor the effects of different MV settings in patients with acute respiratory distress syndrome, allowing more personalized MV. For instance, EIT could help clinicians find the positive end-expiratory pressure that represents a compromise between recruitment and overdistension and assess the effect of prone positioning on ventilation distribution. The clinical impact of the personalization of MV remains to be explored. Despite inherent limitations such as limited spatial resolution, EIT also offers a unique noninvasive bedside assessment of regional ventilation changes in the ICU. This technology offers the possibility of a continuous, operator-free diagnosis and real-time detection of common problems during MV. This review provides an overview of the functioning of EIT, its main indices, and its performance in monitoring patients with acute respiratory failure. Future perspectives for use in intensive care are also addressed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".