State of the Evidence for Emergency Medical Services (EMS) Care: The Evolution and Current Methodology of the Prehospital Evidence-Based Practice (PEP) Program
Bibliographic record
Abstract
Background: Emergency medical services (EMS) leaders and clinicians need to incorporate evidence into safe and effective clinical practice. Access to high-quality evidence, and the time to synthesize it, can be barriers to evidence-based practice. The Prehospital Evidence-Based Practice (PEP) program is an online, freely accessible, repository of critically appraised evidence specific to EMS. This paper describes the evolution and current methodology of the PEP program. Methods|design: The purpose of PEP is to identify, catalog and critically appraise relevant studies. Following regular systematic searches, two trained appraisers critically appraise included studies and assign a score on three-point level of evidence (LOE) and direction of evidence (DOE) scales. Each clinical intervention is plotted on a 3 × 3 (LOE × DOE) evidence matrix, which provides a summary recommendation. Discussion: The PEP program is a unique knowledge translation tool, specific to EMS. End-users can easily identify which clinical interventions are, or are not, supported by evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.417 | 0.681 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.049 | 0.032 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".