Rules of anti-infection therapy for sepsis and septic shock
Bibliographic record
Abstract
OBJECTIVE: Sepsis is a deadly infection that causes injury to tissues and organs. Infection and anti-infective treatment are the eternal themes of sepsis. The successful control of infection is a key factor of resuscitation for sepsis and septic shock. This review examines evidence for the treatment of sepsis. This evidence is combined with clinical experiments to reveal the rules and a standard flowchart of anti-infection therapy for sepsis. DATA SOURCES: We retrieved information from the PubMed database up to October 2018 using various search terms and their combinations, including sepsis, septic shock, infection, antibiotics, and anti-infection. STUDY SELECTION: We included data from peer-reviewed journals printed in English on the relationships between infections and antibiotics. RESULTS: By combining the literature review and clinical experience, we propose a 6Rs rule for sepsis and septic shock management: right patients, right time, right target, right antibiotics, right dose, and right source control. This rule encompasses rational decisions regarding the timing of treatment, the identification of the correct pathogen, the selection of appropriate antibiotics, the formulation of a scientifically based antibiotic dosage regimen, and the adequate control of infectious foci. CONCLUSIONS: This review highlights how to recognize and treat sepsis and septic shock and provides rules and a standard flowchart for anti-infection therapy for sepsis and septic shock for use in the clinical setting.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".