Visualizing the Propagation of Acute Lung Injury
Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation worsens acute respiratory distress syndrome, but this secondary "ventilator-associated" injury is variable and difficult to predict. The authors aimed to visualize the propagation of such ventilator-induced injury, in the presence (and absence) of a primary underlying lung injury, and to determine the predictors of propagation. METHODS: Anesthetized rats (n = 20) received acid aspiration (hydrochloric acid) followed by ventilation with moderate tidal volume (V(T)). In animals surviving ventilation for at least 2 h, propagation of injury was quantified by using serial computed tomography. Baseline lung status was assessed by oxygenation, lung weight, and lung strain (V(T)/expiratory lung volume). Separate groups of rats without hydrochloric acid aspiration were ventilated with large (n = 10) or moderate (n = 6) V(T). RESULTS: In 15 rats surviving longer than 2 h, computed tomography opacities spread outward from the initial site of injury. Propagation was associated with higher baseline strain (propagation vs. no propagation [mean ± SD]: 1.52 ± 0.13 vs. 1.16 ± 0.20, P < 0.01) but similar oxygenation and lung weight. Propagation did not occur where baseline strain was less than 1.29. In healthy animals, large V(T) caused injury that was propagated inward from the lung periphery; in the absence of preexisting injury, propagation did not occur where strain was less than 2.0. CONCLUSIONS: Compared with healthy lungs, underlying injury causes propagation to occur at a lower strain threshold and it originates at the site of injury; this suggests that tissue around the primary lesion is more sensitive. Understanding how injury is propagated may ultimately facilitate a more individualized monitoring or management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".