The use of electrical impedance tomography for individualized ventilation strategy in COVID-19: a case report
Bibliographic record
Abstract
BACKGROUND: Clinical management of COVID-19 requires close monitoring of lung function. While computed tomography (CT) offers ideal way to identify the phenotypes, it cannot monitor the patient response to therapeutic interventions. We present a case of ventilation management for a COVID-19 patient where electrical impedance tomography (EIT) was used to personalize care. CASE PRESENTATION: The patient developed acute respiratory distress syndrome, required invasive mechanical ventilation, and was subsequently weaned. EIT was used multiple times: to titrate the positive end-expiratory pressure, understand the influence of body position, and guide the support levels during weaning and after extubation. We show how EIT provides bedside monitoring of the patient´s response to various therapeutic interventions and helps guide treatments. CONCLUSION: EIT provides unique information that may help the ventilation management in the pandemic of COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".