A National Model for Developing, Implementing, and Evaluating Evidence‐based Guidelines for Prehospital Care
Bibliographic record
Abstract
In 2007, the Institute of Medicine's (IOM's) Committee on the Future of Emergency Care recommended that a multidisciplinary panel establish a model for developing evidence-based protocols for the treatment of emergency medical systems (EMS) patients. In response, the National EMS Advisory Council (NEMSAC) and the Federal Interagency Committee on EMS (FICEMS) convened a panel of multidisciplinary experts to review current strategies for developing evidence-based guidelines (EBGs) and to propose a model for developing such guidelines for the prehospital milieu. This paper describes the eight-step model endorsed by FICEMS, NEMSAC, and a panel of EMS and evidence-based medicine experts. According to the model, prehospital EBG development would begin with the input of evidence from various external sources. Potential EBG topics would be suggested following a preliminary evidentiary review; those topics with sufficient extant foundational evidence would be selected for development. Next, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology would be used to determine a quality-of-evidence rating and a strength of recommendation related to the patient care guidelines. More specific, contextualized patient care protocols would then be generated and disseminated to the EMS community. After educating EMS professionals using targeted teaching materials, the protocols would be implemented in local EMS systems. Finally, effectiveness and uptake would be measured with integrated quality improvement and outcomes monitoring systems. The constituencies and experts involved in the model development process concluded that the use of such transparent, objective, and scientifically rigorous guidelines could significantly increase the quality of EMS care in the future.
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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 | 0.392 | 0.278 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.015 | 0.020 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".