Regional cardiac tissue oxygenation as a function of blood flow and pO[sub 2]: a near-infrared spectroscopic imaging study
Bibliographic record
Abstract
Near-infrared spectroscopic imaging (NIRSI) is useful to assess cardiac tissue oxygenation in arrested and beating hearts, and it shows potential as an intraoperative gauge of the effectiveness of bypass grafting. The purpose of this study was to determine whether NIRSI can reliably differentiate among a range of cardiac oxygenation states, using ischemia and hypoxia models independently. An ischemia-reperfusion model was applied to isolated, beating, blood-perfused porcine hearts, in which the left anterior descending (LAD) artery was cannulated. LAD flow was decreased stepwise to approximately 50, 20, and 0% of normal flow and was completely restored between ischemic episodes. Upon completion of the ischemia-reperfusion protocol, the hearts were further subjected to periods of increasingly severe global hypoxia. Regional oxy- and deoxy-hemoglobin (myoglobin) levels were derived from spectroscopic images (650 to 1050 nm) acquired at each step. Oxygenation maps vividly highlighted the area at risk for all degrees of ischemia. Oxygenation values differed significantly for different LAD flow rates, regardless of whether intermediate reperfusion was applied, and oxygenation values during progressive hypoxia correlated well with blood oxygen saturation. These results suggest that NIRSI is well suited, not only to identify ischemic or hypoxic regions of cardiac tissue, but also to assess the severity of deoxygenation.
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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 | 0.000 |
| 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) | 0.001 | 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".