Bibliographic record
Abstract
PURPOSE OF REVIEW: Hypercapnia is a central component of diverse respiratory disorders, while 'permissive hypercapnia' is frequently used in ventilatory strategies for patients with severe respiratory failure. This review will present data from recent studies relating to hypercapnia, focusing on issues that are of importance to anesthesiologists caring for the surgical and/or critically ill patient. RECENT FINDINGS: Protective ventilatory strategies involving permissive hypercapnia are widely used in patients with severe respiratory failure, particularly in acute respiratory distress syndrome, status asthmaticus, chronic obstructive pulmonary disease and neonatal respiratory failure. The physiologic effects of hypercapnia are increasingly well understood, and important recent insights have emerged regarding the cellular and molecular mechanisms of action of hypercapnia and acidosis. Acute hypercapnic acidosis is protective in multiple models of nonseptic lung injury. These effects are mediated in part through inhibition of the NF-κB pathway. Hypercapnia-mediated NF-κB inhibition may also explain several deleterious effects, including delayed epithelial wound healing and decreased bacterial killing, which has been demonstrated to cause worse lung injury in prolonged untreated pneumonia models. SUMMARY: The mechanisms of action of hypercapnia and acidosis continue to be elucidated, and this knowledge is central to determining the safety and therapeutic utility of hypercapnia in protective lung ventilatory strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".