Unstable Inflation Causing Injury. Insight from Prone Position and Paired Computed Tomography Scans
Bibliographic record
Abstract
RATIONALE: It remains unclear how prone positioning improves survival in acute respiratory distress syndrome. Using serial computed tomography (CT), we previously reported that "unstable" inflation (i.e., partial aeration with large tidal density swings, indicating increased local strain) is associated with injury progression. OBJECTIVES: We prospectively tested whether prone position contains the early propagation of experimental lung injury by stabilizing inflation. METHODS: Injury was induced by tracheal hydrochloric acid in rats; after randomization to supine or prone position, injurious ventilation was commenced using high tidal volume and low positive end-expiratory pressure. Paired end-inspiratory (EI) and end-expiratory (EE) CT scans were acquired at baseline and hourly up to 3 hours. Each sequential pair (EI, EE) of CT images was superimposed in parametric response maps to analyze inflation. Unstable inflation was then measured in each voxel in both dependent and nondependent lung. In addition, five pigs were imaged (EI and EE) prone versus supine, before and (1 hour) after hydrochloric acid aspiration. MEASUREMENTS AND MAIN RESULTS: In rats, prone position limited lung injury propagation and increased survival (11/12 vs. 7/12 supine; P = 0.01). EI-EE densities, respiratory mechanics, and blood gases deteriorated more in supine versus prone rats. At baseline, more voxels with unstable inflation occurred in dependent versus nondependent regions when supine (41 ± 6% vs. 18 ± 7%; P < 0.01) but not when prone. In supine pigs, unstable inflation predominated in dorsal regions and was attenuated by prone positioning. CONCLUSIONS: Prone position limits the radiologic progression of early lung injury. Minimizing unstable inflation in this setting may alleviate the burden of acute respiratory distress syndrome.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".