Hypercapnia Improves Tissue Oxygenation
Bibliographic record
Abstract
BACKGROUND: Wound infections are common, serious, surgical complications. Oxidative killing by neutrophils is the primary defense against surgical pathogens and increasing intraoperative tissue oxygen tension markedly reduces the risk of such infections. Since hypercapnia improves cardiac output and peripheral tissue perfusion, we tested the hypothesis that peripheral tissue oxygenation increases as a function of arterial carbon dioxide tension (PaCO(2)) in anesthetized humans. METHODS: General anesthesia was induced with propofol and maintained with sevoflurane in 30% oxygen in 10 healthy volunteers. Subcutaneous tissue oxygen tension (PsqO(2)) was recorded from a subcutaneous tonometer. An oximeter probe on the upper arm measured muscle oxygen saturation. Cardiac output was monitored noninvasively. PaCO(2) was adjusted to 20, 30, 40, 50, or 60 mmHg in random order with each concentration being maintained for 45 min.(2) (2) RESULTS: Increasing PaCO(2) linearly increased cardiac index and PsqO(2) : PsqO(2) = 35.42 + 0.77 (PaCO(2)), < 0.001. CONCLUSIONS: The observed difference in PsqO(2) is clinically important because previous work suggests that comparable increases in tissue oxygenation reduced the risk of surgical infection from -8% to 2 to 3%. We conclude that mild intraoperative hypercapnia increased peripheral tissue oxygenation in healthy human subjects, which may improve resistance to surgical wound infections.
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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) | 0.000 | 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) | 0.003 | 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".