Evidence-Based Guidelines for Prehospital Pain Management: Literature and Methods
Bibliographic record
Abstract
INTRODUCTION: Emergency Medical Services (EMS) clinicians commonly encounter patients with acute pain. A new set of evidence-based guidelines (EBG) was developed to assist in the prehospital management of pain. Our objective was to describe the methods used to develop these evidence-based guidelines for prehospital pain management. METHODS: The EBG development process was supported by a previous systematic review conducted by the Agency for Healthcare Research and Quality (AHRQ) covering nine different population, intervention, comparison, and outcome (PICO) questions. A technical expert panel (TEP) was formed and added an additional pediatric-specific PICO question. Identified evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework and tabulated into Summary of Findings tables. The TEP then utilized a rigorous systematic method, including the PanelVoice function, for recommendation development which was applied to generate Evidence to Decision Tables (EtD). This process involved review of the Summary of Findings tables, asynchronous member judging, and facilitated panel discussion to generate final consensus-based recommendations. RESULTS: The work product described above was completed by the TEP panel from September 2020 to April 2021. For these recommendations, the overall certainty of evidence was very low or low, data for decisions on cost effectiveness and equity were lacking, and feasibility was rated well across all categories. Based on the evidence, one strong and seven conditional recommendations were made, with two PICO questions lacking sufficient evidence to generate a recommendation. CONCLUSION: We describe a protocol that leveraged established EBG development techniques, the GRADE framework in conjunction with a previous AHRQ systematic review to develop treatment recommendations for prehospital pain management. This process allowed for mitigation of many confounders due to the use of virtual and electronic communication. Our approach may inform future guideline development and increase transparency in the prehospital recommendations development processes.
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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 | 0.088 | 0.274 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.050 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".