Precision Medicine Using Simultaneous Monitoring and Assessment with Imaging and Biomarkers to Manage Mechanical Ventilation in ARDS
Bibliographic record
Abstract
Acute respiratory distress syndrome (ARDS) has a ~ 40% mortality rate with an increasing prevalence exacerbated by the COVID-19 pandemic. Mechanical ventilation is the primary means for life-saving support to buy time for lung healing in ARDS patients, however, it can also lead to ventilator-induced lung injury (VILI). Effective strategies to reduce or prevent VILI are necessary but are not currently delivered. Therefore, we aim at evaluating the current imaging technologies to visualize where pressure and volume being delivered to the lung during mechanical ventilation; and combining plasma biomarkers to guide management of mechanical ventilation. We searched PubMed and Medline using keywords and analyzed the literature, including both animal models and human studies, to examine the independent use of computed tomography (CT) to evaluate lung mechanics, electrical impedance tomography (EIT) to guide ventilation, ultrasound to monitor lung injury, and plasma biomarkers to indicate status of lung pathophysiology. This investigation has led to our proposal of the combination of imaging and biomarkers to precisely deliver mechanical ventilation to improve patient outcomes in ARDS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".