Defining adverse events during trauma resuscitation: a modified RAND Delphi study
Bibliographic record
Abstract
BACKGROUND: The majority of preventable adverse event (AEs) in trauma care occur during the initial phase of resuscitation, often within the trauma bay. However, there is significant heterogeneity in reporting these AEs that limits performance comparisons between hospitals and trauma systems. The objective of this study was to create a taxonomy of AEs that occur during trauma resuscitation and a corresponding classification system to assign a degree of harm. METHODS: This study used a modified RAND Delphi methodology to establish a taxonomy of AEs in trauma and a degree of harm classification system. A systematic review informed the preliminary list of AEs. An interdisciplinary panel of 22 trauma experts rated these AEs through two rounds of online surveys and a final consensus meeting. Consensus was defined as 80% for each AE and the final checklist. RESULTS: The Delphi panel consisted of 22 multidisciplinary trauma experts. A list of 57 evidence-informed AEs was revised and expanded during the modified Delphi process into a finalized list of 67 AEs. Each AE was classified based on degree of harm on a scale from I (no harm) to V (death). DISCUSSION: This study developed a taxonomy of 67 AEs that occur during the initial phases of a trauma resuscitation with a corresponding degree of harm classification. This taxonomy serves to support a standardized evaluation of trauma care between centers and regions. LEVEL OF EVIDENCE: Level 5.
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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 | 0.179 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".