State of the Evidence for Emergency Medical Services Care of Adult Patients with Sepsis: An Analysis of Research from the Prehospital Evidence-Based Practice Program
Bibliographic record
Abstract
Introduction The Prehospital Evidence-Based Practice (PEP) program is an online, freely accessible, continuously updated emergency medical services evidence repository. This PEP summary describes the research evidence for the identification and management of adult patients with sepsis or septic shock. Methods A systematic search of the literature on sepsis or septic shock was conducted. Studies were scored by trained appraisers on a three-point level of evidence scale (based on study design and quality) and a three-point direction of evidence scale (supportive, neutral or opposing findings based on the studies’ primary outcome for each intervention). Results One hundred forty-three studies (80 existing and 63 new) were included for 16 interventions listed in PEP for adult patients with sepsis. The evidence matrix rank for supported interventions (n=16) were supportive-high quality (n=2, 12.5%) for crystalloid infusion and vasopressors, supportive-moderate quality (n=8, 50%) for identification tools, pre-notification, point-of-care lactate, titrated oxygen, temperature monitoring and balanced crystalloids. The benefit of pre-hospital antibiotics, colloids, Trendelenburg position and early goal-directed therapy remain inconclusive with a neutral direction of evidence. There is moderate level evidence opposing the use of high flow oxygen. Conclusion Several standard treatments are well supported by the evidence including fluid resuscitation, using balanced crystalloids, vasopressors and titrating oxygen. Tools for identifying and guiding treatment are also supported (eg. pre-notification, temperature monitoring and lactate). The evidence for antibiotic use is inconclusive. This PEP state of the evidence analysis can be used to guide selection of appropriate pre-hospital therapies during the development of pre-hospital protocols or clinical practice guidelines.
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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 | 0.133 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.040 | 0.034 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".