Infectious Diseases Team for the Early Management of Severe Sepsis and Septic Shock in the Emergency Department
Bibliographic record
Abstract
Background: The impact on patient survival of an infectious disease (ID) team dedicated to the early management of severe sepsis/septic shock (SS/SS) in Emergency Department (ED) has yet to be assessed. Methods: A quasiexperimental pre-post study was performed at the general ED of our hospital. During the pre phase (June 2013-July 2014), all consecutive adult patients with SS/SS were managed according to the standard of care, data were prospectively collected. During the post phase (August 2014-October 2015), patients were managed in collaboration with a dedicated ID team performing a bedside patient evaluation within 1 hour of ED arrival. Results: Overall, 382 patients were included, 195 in the pre phase and 187 in the post phase. Median age was 82 years (interquartile range, 70-88). The most common infection sources were lung (43%) and urinary tract (17%); in 22% of cases, infection source remained unknown. During the post phase, overall compliance with the Surviving Sepsis Campaign (SSC) bundle and appropriateness of initial antibiotic therapy improved from 4.6% to 32% (P < .001) and from 30% to 79% (P < .001), respectively. Multivariate analysis showed that predictors of all-cause 14-day mortality were quick sepsis-related organ failure assessment ≥2 (hazard ratio [HR], 1.68; 95% confidence interval [CI], 1.15-2.45; P = .007), serum lactate ≥2 mmol/L (HR, 2.13; 95% CI, 1.39-3.25; P < .001), and unknown infection source (HR, 2.07; 95% CI, 1.42-3.02; P < .001); being attended during the post phase was a protective factor (HR, 0.64; 95% CI, 0.43-0.94; P = .026). Conclusion: Implementation of an ID team for the early management of SS/SS in the ED improved the adherence to SSC recommendations and patient survival.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".