Mild loss of lung aeration augments stretch in healthy lung regions
Bibliographic record
Abstract
Inspiratory stretch by mechanical ventilation worsens lung injury. However, it is not clear whether and how the ventilator damages lungs in the absence of preexisting injury. We hypothesized that subtle loss of lung aeration during general anesthesia regionally augments ventilation and distension of ventilated air spaces. In eight supine anesthetized and intubated rats, hyperpolarized gas MRI was performed after a recruitment maneuver following 1 h of volume-controlled ventilation with zero positive end-expiratory pressure (ZEEP), FiO2 0.5, and tidal volume 10 ml/kg, and after a second recruitment maneuver. Regional fractional ventilation (FV), apparent diffusion coefficient (ADC) of (3)He (a measurement of ventilated peripheral air space dimensions), and gas volume were measured in lung quadrants of ventral and dorsal regions of the lungs. In six additional rats, computed tomography (CT) images were obtained at each time point. Ventilation with ZEEP decreased total lung gas volume and increased both FV and ADC in all studied regions. Increases in FV were more evident in the dorsal slices. In each lung quadrant, higher ADC was predicted by lower gas volume and by increased mean values (and heterogeneity) of FV distribution. CT scans documented 10% loss of whole-lung aeration and increased density in the dorsal lung, but no macroscopic atelectasis. Loss of pulmonary gas at ZEEP increased fractional ventilation and inspiratory dimensions of ventilated peripheral air spaces. Such regional changes could help explain a propensity for mechanical ventilation to contribute to lung injury in previously uninjured lungs.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".