Mathematical Model of Tissue Oxygenation in Early Sepsis
Bibliographic record
Abstract
The defining characteristic of sepsis is a progressive microvascular dysfunction remote to the locus of infection. Our objective was to quantify the time course of changing tissue PO 2 due to microvascular injury in early sepsis using an established mathematical model. Experimental data obtained from a rat cecal‐ligation and perforation (CLP) model of sepsis has demonstrated a progressive FCD loss at 2, 3 and 4 hours (11±4%, 15±1%, 21±5% SHAM vs 18±3%, 32±5%, 48±7% CLP respectively). Microvascular blood flow was recorded in skeletal muscle using dual wavelength intra‐vital video microscopy. Capillaries were analyzed at baseline in vivo for O 2 saturations and hemodynamics. Custom vascular mapping software was used to reconstruct the real geometry (length, diameter, tissue depth and connections between vessels) of the experimental network (Volume 420 × 420 × 60 μm ). A constant consumption computational model applied to the FCD conditions at each time point found mean tissue PO 2 was not different between SHAM and CLP at 2 hours (34.2±4.3 vs 33.3±4.2 mmHg) or 3 hours (33.0±4.3 vs 31.2±4.5 mmHg) post. At 4 hours tissue PO 2 was significantly different between SHAM and CLP (32.7±4.3 vs 25.9±6.5 mmHg, p < 0.01). This simulation suggests that a substantial FCD loss precedes a significant decrease in tissue oxygenation due to diffusional exchange between adjacent capillaries. Supported by CIHR grant to DG and CGE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".