Bibliographic record
Abstract
Introduction: Severe sepsis and septic shock are associated with a high mortality rate but are under recognized and undertreated. Early antibiotic administration and fluid resuscitation are associated with improved survival. In 2010, the Critical Care Leadership Network of the Greater New York Hospital Association and the United Hospital Fund developed the STOP (Strengthening Treatment and Outcomes for Patients) Sepsis Collaborative—a quality improvement initiative that supports hospitals in the early recognition and treatment of severe sepsis and septic shock using a protocol-based approach in the emergency department and the intensive care unit. Hypothesis: A quality improvement collaborative focused on the implementation of standardized protocols for early identification and treatment of patients with severe sepsis and septic shock in the emergency department can improve treatment process measures and reduce mortality. Methods: Fifty-seven hospitals in the greater New York region participating in a quality improvement collaborative were provided with comprehensive education and tools to recognize patients with severe sepsis and septic shock in the emergency department and implement an evidence-based resuscitation protocol. The intervention period began in January 2011, and included a comprehensive educational program for an interdisciplinary group of clinicians, nurses, and administrators. Data were collected on all sepsis process measures and time stamps were recorded. Participating hospitals also submitted summary patient outcomes. Results: Participating hospitals submitted information on 7,470 patients with severe sepsis admitted between January 2011 and March 2012. Within six months, 91% of participating hospitals had adopted a sepsis identification protocol in the emergency department and sepsis resuscitation protocols were implemented in the emergency department by 93% of the participating hospitals. Between January 2011 and March 2012, inpatient mortality declined from 41% to 26% (absolute mortality reduction of 15%). Conclusions: Hospitals that participated in a quality improvement collaborative designed to assist hospitals in using protocols for early diagnosis and treatment of severe sepsis and septic shock in their emergency departments saw an overall 15% reduction in hospital mortality in patients with these conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.655 | 0.536 |
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".