Rules of anti-infection therapy for sepsis and septic shock
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it