The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2024
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
The 2024 revised edition of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock (J-SSCG 2024) is published by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine. This is the fourth revision since the first edition was published in 2012. The purpose of the guidelines is to assist healthcare providers in making appropriate decisions in the treatment of sepsis and septic shock, leading to improved patient outcomes. We aimed to create guidelines that are easy to understand and use for physicians who recognize sepsis and provide initial management, specialized physicians who take over the treatment, and multidisciplinary healthcare providers, including nurses, physical therapists, clinical engineers, and pharmacists. The J-SSCG 2024 covers the following nine areas: diagnosis of sepsis and source control, antimicrobial therapy, initial resuscitation, blood purification, disseminated intravascular coagulation, adjunctive therapy, post-intensive care syndrome, patient and family care, and pediatrics. In these areas, we extracted 78 important clinical issues. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members. As a result, 42 GRADE-based recommendations, 7 good practice statements, and 22 information-to-background questions were created as responses to clinical questions. We also described 12 future research questions.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it