Carbon dioxide attenuates pulmonary impairment resulting from hyperventilation*
Bibliographic record
Abstract
OBJECTIVE: Deliberate elevation of PaCO2 (therapeutic hypercapnia) protects against lung injury induced by lung reperfusion and severe lung stretch. Conversely, hypocapnic alkalosis causes lung injury and worsens lung reperfusion injury. Alterations in lung surfactant may contribute to ventilator-associated lung injury. The potential for CO2 to contribute to the pathogenesis of ventilator-associated lung injury at clinically relevant tidal volumes is unknown. We hypothesized that: 1) hypocapnia would worsen ventilator-associated lung injury, 2) therapeutic hypercapnia would attenuate ventilator-associated lung injury; and 3) the mechanisms of impaired compliance would be via alteration of surfactant biochemistry. DESIGN: Randomized, prospective animal study. SETTING: Research laboratory of university-affiliated hospital. SUBJECTS: Anesthetized, male New Zealand Rabbits. INTERVENTIONS: All animals received the same ventilation strategy (tidal volume, 12 mL/kg; positive end-expiratory pressure, 0 cm H2O; rate, 42 breaths/min) and were randomized to receive FiCO2 of 0.00, 0.05, or 0.12 to produce hypocapnia, normocapnia, and hypercapnia, respectively. MEASUREMENTS AND MAIN RESULTS: Alveolar-arterial oxygen gradient was significantly lower with therapeutic hypercapnia, and peak airway pressure was significantly higher with hypocapnic alkalosis. However, neither static lung compliance nor surfactant chemistry (total surfactant, aggregates, or composition) differed among the groups. CONCLUSIONS: At clinically relevant tidal volume, CO2 modulates key physiologic indices of lung injury, including alveolar-arterial oxygen gradient and airway pressure, indicating a potential role in the pathogenesis of ventilator-associated lung injury. These effects are surfactant independent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".