Characterization of Impaired Microvascular Oxygen Delivery in Early Septic Injury
Bibliographic record
Abstract
The study of the microvasculature is critical to understanding the impact and progression of a broad range of diseases. Quantifying the changes that occur in the microvasculature during sepsis presents several problems that must be addressed in analysis and experimental approach. The current study explores different strategies that can be used to quantify the dynamic changes in oxygen delivery within a single capillary network over the early time course of sepsis. The progressive loss of flowing capillaries results in a decreasing population of vessels that can be considered for analysis. Combined with increased flow heterogeneity and number of hyperdynamic vessels the challenge of quantifying conditions within the microvasculature becomes compounded. Maldistribution of flow results in decreased oxygen saturation in normally perfused vessels. Extremely fast flow vessels (> 1800μs) present a specific challenge for analysis. Recent computational models have indicated that these vessels play a major role in oxygen delivery. By considering conditions in capillaries adjacent to fast flow vessels, the efficacy of oxygen delivery can be better quantified. The advantages and disadvantages of current characterization strategies will be compared with alternative approaches that combine direct measurements and computational analysis.
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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 | 0.001 | 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".