Microvascular resuscitation as a therapeutic goal in severe sepsis.
Bibliographic record
Abstract
Sepsis causes microvascular dysfunction. Increased heterogeneity of capillary blood flow results in local tissue hypoxia, which can cause local tissue inflammation, impaired oxygen extraction, and, ultimately, organ dysfunction. Microvascular dysfunction is clinically relevant because it is a marker for mortality: it improves rapidly in survivors of sepsis but fails to improve in nonsurvivors. This, along with the fact that resuscitation of mean arterial pressure and cardiac output alone fails to improve microvascular function, means that microvascular resuscitation is therefore a therapeutic goal. In animal studies of sepsis, volume resuscitation improves microvascular permeability and tissue oxygenation, and leads to improved organ function, including a reduction in myocardial dysfunction. Microvascular resuscitation strategies include hemodynamic resuscitation using the linked combination of volume resuscitation, judicious vasopressor use, and inotropes and vasodilators. Alternative vasoactive agents, such as vasopressin, may improve microcirculatory function to a greater degree than conventional vasopressors. Successful modulation of inflammation has a positive impact on endothelial function. Finally, targeted treatment of the endothelium, using activated protein C, also improves microvascular function and ultimately increases survival. Thus, attention must be paid to the microcirculation in patients with sepsis, and therapeutic strategies should be employed to resuscitate the microcirculation in order to avoid organ dysfunction and to reduce mortality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".