Optimizing Antimicrobial Therapy of Sepsis and Septic Shock: Focus on Antibiotic Combination Therapy
Bibliographic record
Abstract
There has been little improvement in septic shock mortality in the past 70 years, despite ever more broad-spectrum and potent antimicrobials. In the past, resuscitative elements have been the primary area of clinical septic shock management and research. The question of the optimal use of antimicrobial therapy was relatively ignored in recent decades. This review explores the pathophysiology of sepsis in an attempt to produce a better understanding and define key determinants of antimicrobial therapy response in septic shock. Optimizing existing antimicrobials delivery can drive significant improvements in the outcome of sepsis and septic shock. Inappropriate antimicrobial selection and dosing or delays in the administration substantially increase mortality and morbidity in life-threatening infections. Definitive combination therapy (where a pathogen known to be susceptible to a given agent is additionally covered by another agent) remains controversial. Although some in vitro studies, animal models, and clinical studies of infection including endocarditis, gram-negative bacteremia, and neutropenic infections have supported combination therapy, the potential clinical benefit in other severe infections has been questioned. Several meta-analyses have failed to demonstrate improvement of outcome with combination therapy in immunocompetent patients with sepsis and/or gram-negative bacteremia. These meta-analyses did not undertake subgroup analyses of the septic shock population. This article reviews the existing evidence supporting combination therapy for severe infections, sepsis, and septic shock.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".