Effect of sternotomy and extracorporeal circulation on pulmonary neutrophil kinetics in pigs
Bibliographic record
Abstract
We evaluated single‐pass pulmonary neutrophil kinetics using the multiple indicator‐dilution technique in control pigs (n = 10), after sternotomy alone (sterno, n = 10) or after 30 min of observation following a period of 90 min extracorporeal circulation (n = 7). Blood neutrophils increased in the control and sterno groups (p < 0.05) but remained unchanged in the extracorporeal circulation group. The transfer coefficient for neutrophil margination from the circulating to the lung‐marginated pool (kc‐m) and pulmonary neutrophil clearance (Clc‐m) were similar between the three groups. There was an inverse correlation between kc‐m and the degree of lung tissue perfusion evaluated form the tracer‐accessible extravascular lung water (r = −0.54, p < 0.01). There was no arterio‐venous gradient of neutrophils in any of the groups, suggesting a dynamic equilibrium of the margination/demargination processes. We conclude that extracorporeal circulation does not significantly modify single pass pulmonary neutrophil kinetics 30 min after reperfusion. The rate of neutrophil margination to the tracer‐accessible lung tissue suggests that lung tissue derecruitment is associated with increased neutrophil margination. This work was supported by the Heart and Stroke Foundation of Canada and the “Fondation de l’Institut de Cardiologie de Montréal”. Dr. Dupuis is a senior scholar of the “Fonds de la recherche en santé du Québec”.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".