Intra‐pulmonary shunt and pulmonary gas exchange during exercise in humans
Bibliographic record
Abstract
In young, healthy people the alveolar-arterial P(O(2)) difference (A-aDO(2)) is small at rest, but frequently increases during exercise. Previously, investigators have focused on ventilation/perfusion mismatch and diffusion abnormalities to explain the impairment in gas exchange, as significant physiological intra-pulmonary shunt has not been found. The aim of this study was to use a non-gas exchange method to determine if anatomical intra-pulmonary (I-P) shunts develop during exercise, and, if so, whether there is a relationship between shunt and increased A-aDO(2). Healthy male participants performed graded upright cycling to 90% while pulmonary arterial (PAP) and pulmonary artery wedge pressures were measured. Blood samples were obtained from the radial artery, cardiac output was calculated by the direct Fick method and I-P shunt was determined by administering agitated saline during continuous 2-D echocardiography. A-aDO(2) progressively increased with exercise and was related to (r = 0.86) and PAP (r = 0.75). No evidence of I-P shunt was found at rest in the upright position; however, 7 of 8 subjects developed I-P shunts during exercise. In these subjects, point bi-serial correlations indicated that I-P shunts were related to the increased A-aDO(2) (r = 0.68), (r = 0.76) and PAP (r = 0.73). During exercise, intra-pulmonary shunt always occurred when A-aDO(2) exceeded 12 mmHg and was greater than 24 l min(-1). These results indicate that anatomical I-P shunts develop during exercise and we suggest that shunt recruitment may contribute to the widened A-aDO(2) during exercise.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".