The Resuscitation, Equilibrium and De-escalation (RED) strategy: a phased, personalized hemodynamic support in children with sepsis
Bibliographic record
Abstract
Hemodynamic support in critically ill children with septic shock is a pervasive challenge in the intensive care settings. Cardiovascular involvement in sepsis entails both macro- and microcirculation abnormalities, with the main treatment objectives seeking to increase cardiac output and improve tissue perfusion, respectively. Fluid therapy and vasoactive drugs are cornerstone therapies for circulatory problems in sepsis. Fluid boluses are a common first-line treatment for actual and relative hypovolemia. However, their use has been linked to adverse events due to factors such as their composition, high volumes and rapid infusion rates, and the variable response of individual patients. Furthermore, they often have transient efficacy or lack of response in many patients. Vasoactive drugs are also often used late, which favors repetitive fluid boluses, leading to hypervolemia, tissue edema and worse outcomes. After the resuscitation phase, active fluid removal through diuresis or dialysis is increasingly being used in patients who receive fluid therapy, but it has not yet been standardized, and the safest and most effective strategies in children are still not known. We believe that these interventions for hemodynamic problems in sepsis offer an opportunity to personalize treatment and apply precision medicine strategies. Using a phased approach adapted to each patient's context and clinical condition can potentially improve outcomes. The proposed Resuscitation, Equilibrium and De-escalation (RED) strategy is a simplified phased hemodynamic management approach for patients with sepsis and septic shock. Our goal with the introduction of this concept is to organize and underscore the fact that the cardiovascular support of sepsis is dynamic and should be adapted to each individual and context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".