An Evidence-based Guideline for Prehospital Analgesia in Trauma
Bibliographic record
Abstract
BACKGROUND: The management of acute traumatic pain is a crucial component of prehospital care and yet the assessment and administration of analgesia is highly variable, frequently suboptimal, and often determined by consensus-based regional protocols. OBJECTIVE: To develop an evidence-based guideline (EBG) for the clinical management of acute traumatic pain in adults and children by advanced life support (ALS) providers in the prehospital setting. Methods. We recruited a multi-stakeholder panel with expertise in acute pain management, guideline development, health informatics, and emergency medical services (EMS) outcomes research. Representatives of the National Highway Traffic Safety Administration (sponsoring agency) and a major children's research center (investigative team) also contributed to the process. The panel used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to guide the process of question formulation, evidence retrieval, appraisal/synthesis, and formulation of recommendations. The process also adhered to the National Prehospital Evidence-Based Guideline (EBG) model process approved by the Federal Interagency Council for EMS and the National EMS Advisory Council. RESULTS: Four strong and three weak recommendations emerged from the process; two of the strong recommendations were linked to high- and moderate-quality evidence, respectively. The panel recommended that all patients be considered candidates for analgesia, regardless of transport interval, and that opioid medications should be considered for patients in moderate to severe pain. The panel also recommended that all patients should be reassessed at frequent intervals using a standardized pain scale and that patients should be re-dosed if pain persists. The panel suggested the use of specific age-appropriate pain scales. CONCLUSION: GRADE methodology was used to develop an evidence-based guideline for prehospital analgesia in trauma. The panel issued four strong recommendations regarding patient assessment and narcotic medication dosing. Future research should define optimal approaches for implementation of the guideline as well as the impact of the protocol on safety and effectiveness metrics.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".