Injurious Effects of Hypocapnic Alkalosis in the Isolated Lung
Bibliographic record
Abstract
Mechanical ventilation can worsen morbidity and mortality by causing ventilator-associated lung injury, especially where adverse ventilatory strategies are employed. Adverse strategies commonly involve hyperventilation, which frequently results in hypocapnia. Although hypocapnia is associated with significant lung alterations (e.g., bronchospasm, airway edema), the effects on alveolar-capillary permeability are unknown. We investigated whether hypocapnia could cause lung injury independent of altering ventilatory strategy. We hypothesized that hypocapnia would cause lung injury during prolonged ventilation, and would worsen injury following ischemia-reperfusion. We utilized the isolated buffer-perfused rabbit lung model. Pilot studies assessed a range of levels of hypocapnic alkalosis. Experimental preparations were randomized to control groups (FI(CO(2)) = 0.06) or groups with hypocapnia (FI(CO(2)) = 0.01). Following prolonged ventilation, pulmonary artery pressure, airway pressure, and lung weight were unchanged in the control group but were elevated in the group with hypocapnia; elevation in microvascular permeability was greater in the hypocapnia versus control groups. Injury following ischemia-reperfusion was significantly worse in the hypocapnia versus control groups. In a preliminary series, degree of lung injury was proportional to the degree of hypocapnic alkalosis. We conclude that in the current model (1) hypocapnic alkalosis is directly injurious to the lung and (2) hypocapnic alkalosis potentiates ischemia-reperfusion-induced acute lung injury.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".