Prehospital management and identification of sepsis by emergency medical services: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To identify studies describing the accuracy of prehospital sepsis identification and to summarise results of studies of prehospital management of patients with sepsis, severe sepsis or septic shock. METHODS: We conducted a systematic review to retrieve studies that evaluated the prehospital identification or treatment of patients with sepsis by emergency medical services (EMS). Two authors extracted data describing the study characteristics, incidence of sepsis among EMS-transported patients, criteria used to identify sepsis and specific treatments provided to patients with sepsis. When possible, we calculated the sensitivity and specificity of EMS provider diagnosis of sepsis. RESULTS: Our search identified no randomised controlled trials and 16 cohort studies. Eight studies described the identification of sepsis, seven described prehospital management or treatment of sepsis and one described both. The most common approach to the identification of sepsis involved applying systemic inflammatory response syndrome criteria or a combination of vital signs, which had sensitivity ranging from 0.43 to 0.86 when used alone or combined with provider impression. Only four studies collected information required to calculate specificity (0.47-0.87). Meta-analysis was not performed owing to significant heterogeneity and an overall low quality of evidence. A few studies described prehospital sepsis treatment-most commonly intravenous fluid resuscitation. CONCLUSIONS: The evidence suggests that identification of sepsis in the prehospital setting by EMS providers is carried out with varied success, depending on the strategy used; however, high-quality studies are lacking. Relying on provider impression alone had poor sensitivity, but some moderate-quality evidence supporting structured screening for sepsis with vital signs criteria demonstrated modest sensitivity and specificity. Additional research to improve diagnostic accuracy and explore improvements in EMS management is needed.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".