Hemodynamic support in the early phase of septic shock: a review of challenges and unanswered questions
Bibliographic record
Abstract
BACKGROUND: Improving sepsis support is one of the three pillars of a 2017 resolution according to the World Health Organization (WHO). Septic shock is indeed a burden issue in the intensive care units. Hemodynamic stabilization is a cornerstone element in the bundle of supportive treatments recommended in the Surviving Sepsis Campaign (SSC) consecutive biannual reports. MAIN BODY: The "Pandera's box" of septic shock hemodynamics is an eternal debate, however, with permanent contentious issues. Fluid resuscitation is a prerequisite intervention for sepsis rescue, but selection, modalities, dosage as well as duration are subject to discussion while too much fluid is associated with worsen outcome, vasopressors often need to be early introduced in addition, and catecholamines have long been recommended first in the management of septic shock. However, not all patients respond positively and controversy surrounding the efficacy-to-safety profile of catecholamines has come out. Preservation of the macrocirculation through a "best" mean arterial pressure target is the actual priority but is still contentious. Microcirculation recruitment is a novel goal to be achieved but is claiming more knowledge and monitoring standardization. Protection of the cardio-renal axis, which is prevalently injured during septic shock, is also an unavoidable objective. Several promising alternative or additive drug supporting avenues are emerging, trending toward catecholamine's sparing or even "decatecholaminization." Topics to be specifically addressed in this review are: (1) mean arterial pressure targeting, (2) fluid resuscitation, and (3) hemodynamic drug support. CONCLUSION: Improving assessment and means for rescuing hemodynamics in early septic shock is still a work in progress. Indeed, the bigger the unresolved questions, the lower the quality of evidence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".