Lung Ventilation and Perfusion in Prone and Supine Postures with Reference to Anesthetized and Mechanically Ventilated Healthy Volunteers
Bibliographic record
Abstract
BACKGROUND: The literature on ventilation (V) and lung perfusion (Q) distributions during general anesthesia and controlled mechanical ventilation in supine and prone position is contradictory. The authors aimed to investigate whether V, Q, and ventilation to perfusion ratio (V/Q ratio) matching in anesthetized and mechanically ventilated volunteers are gravity dependent irrespective of posture. METHODS: Seven healthy volunteers were studied at two different occasions during general anesthesia and controlled mechanical ventilation. One occasion studied ventral to dorsal V and Q distributions in the supine posture and the other in the prone posture. Imaging was performed in supine posture at both occasions. A dual radiotracer technique and single photon emission computed tomography were used. V and Q were simultaneously tagged with Tc-Technegas (Tetley Manufacturing Ltd., Sydney, Australia) and In-labeled macroaggregates of human albumin (TechneScan LyoMAA, Mallinckrodt Medica, Petten, The Netherlands), respectively. RESULTS: No differences in V between postures were observed. Q differed between postures, being more uniform over different lung regions in prone posture and dependent in supine posture. The contribution of the vertical direction to the total V/Q ratio heterogeneity was larger in supine (31.4%) than in prone (16.4%) (P = 0.0639, two-tailed, paired t test) posture. CONCLUSIONS: During mechanical ventilation, prone posture favors a more evenly distributed Q between lung regions. V distribution is independent of posture. This results in a tendency toward lower V/Q gradients in the ventral to dorsal direction in prone compared with supine posture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".