Sepsis now a priority: a quality improvement initiative for early sepsis recognition and care
Bibliographic record
Abstract
OBJECTIVE: To develop a triage-based screening algorithm and treatment order-sets aimed at improving the quality of care of all patients with sepsis presenting to our emergency department (ED). DESIGN: Retrospective cohort study conducted during a pre-intervention period from 1 April 2010 to 31 March 2011 and a post-intervention period from 1 September 2014 to 30 April 2015. SETTING: A large teaching hospital located in Toronto, Ontario, Canada with a 35-bed ED. PARTICIPANTS: All patients meeting pre-specified sepsis criteria during the ED encounter. MAIN OUTCOME MEASURES: Process of care measures included time to assessment by emergency physician, lactate measurement, blood culture collection, fluid and antibiotic administration. Intensive care unit (ICU) outcomes including admissions, length of stay (LOS) and deaths were reviewed. RESULTS: There were 346 patients pre-intervention, and 270 patients post-intervention. We significantly improved all process measures including mean time to antibiotics by 60 min (P = 0.003) and proportion of patients receiving fluid resuscitation (64.7% vs. 94.4%, P < 0.001). There was no significant difference in the number of patients admitted to ICU (P = 0.14). The median ICU LOS was shorter in the post-intervention group [2.0 days (interquartile range (IQR) 1.0-4.5 days) vs. 5.0 days (IQR 1.5-10.8 days), P = 0.04], and there was no difference in in-hospital mortality between groups (P = 0.27). CONCLUSIONS: We have demonstrated that a triage-based sepsis screening tool results in expedited and consistent delivery of care, with a significant improvement in initial resuscitation measures.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".