SPECIAL CONTRIBUTIONS The Canadian Prehospital Evidence-based Protocols Project: Knowledge Translation in Emergency Medical Services Care
Bibliographic record
Abstract
Objectives: The principles of evidence-based medicine are applicable to all areas and professionals in health care. The care provided by paramedics in the prehospital setting is no exception. The Prehospital Evidence-based Protocols Project Online (PEP) is a repository of appraised research evidence that is applicable to interventions performed in the prehospital setting and is openly available online. This arti-cle describes the history, current status, and potential future of the project. Methods: The primary objective of the PEP is to catalog and grade emergency medical services (EMS) studies with a level of evidence (LOE). Subsequently, each prehospital intervention is assigned a class of recommendation (COR) based on all the appraised articles on that intervention, in an effort to organize the evidence so it may be put into practice efficiently. An LOE is assigned to each article by the section editor, based on the study rigor and applicability to EMS. The section editor committee consists of EMS physicians and paramedics from across Canada, and two from Ireland and a paramedic coordinator. The evidence evaluation cycle is continuous; as the section editors send back appraisals, the coordinator updates the database and sends out another article for review. Results: The database currently has 182 individual interventions organized under 103 protocols, with 933 citations. Conclusions: This project directly meets recent recommendations to improve EMS by using evidence to support interventions and incorporating it into protocols. Organizing and grading the evidence allows medical directors and paramedics to incorporate research findings into their daily practice. As such, this project demonstrates how knowledge translation can be conducted in EMS.
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 | 0.162 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.012 |
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".