Evaluation of the Benefits of De-Escalation for Patients with Sepsis in the Emergency Intensive Care Unit
Bibliographic record
Abstract
PURPOSE: Although the 2016 Japanese guidelines for the management of sepsis recommend de-escalation of treatment after identification of the causative pathogen, adherence to this practice remain unknown. The objective of this study was to evaluate the benefits of de-escalating treatment for sepsis patients at an advanced critical care and emergency medical centre. METHODS: Based on electronic patient information, 85 patients who were transported to the centre by ambulance, and diagnosed with sepsis between January 2008 and September 2013 were enrolled and evaluated. Patients were divided into two groups with and without de-escalation, and comparisons were conducted for several variables, including length of hospital stay, and length of antibiotic administration. Two types of subgroup analysis were conducted between patients with septic shock or positive blood cultures. Statistical analysis was conducted using chi-square and Mann-Whitney U tests. RESULTS: The length of hospital stay after diagnosis was significantly shorter for the de-escalation group than for the non-de-escalation group. In the subgroup analysis, de-escalation for blood culture-positive patients was beneficial in terms of the length of hospital stay and length of antibiotic administration. CONCLUSIONS: The findings of this study suggest that sepsis treatment de-escalation is beneficial for treatment efficacy and appropriate use of antibiotics. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".