Development of the major trauma case review tool
Bibliographic record
Abstract
BACKGROUND: As many as half of all patients with major traumatic injuries do not receive the recommended care, with variance in preventable mortality reported across the globe. This variance highlights the need for a comprehensive process for monitoring and reviewing patient care, central to which is a consistent peer-review process that includes trauma system safety and human factors. There is no published, evidence-informed standardised tool that considers these factors for use in adult or paediatric trauma case peer-review. The aim of this research was to develop and validate a trauma case review tool to facilitate clinical review of paediatric trauma patient care in extracting information to facilitate monitoring, inform change and enable loop closure. METHODS: Development of the trauma case review tool was multi-faceted, beginning with a review of the trauma audit tool literature. Data were extracted from the literature to inform iterative tool development using a consensus approach. Inter-rater agreement was assessed for both the pilot and finalised versions of the tool. RESULTS: The final trauma case review tool contained ten sections, including patient factors (such as pre-existing conditions), presenting problem, a timeline of events, factors contributing to the care delivery problem (including equipment, work environment, staff action, organizational factors), positive aspects of care and the outcome of panel discussion. After refinement, the inter-rater reliability of the human factors and outcome components of the tool improved with an average 86% agreement between raters. DISCUSSION: This research developed an evidence-informed tool for use in paediatric trauma case review that considers both system safety and human factors to facilitate clinical review of trauma patient care. CONCLUSIONS: This tool can be used to identify opportunities for improvement in trauma care and guide quality assurance activities. Validation is required in the adult population.
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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 | 0.200 | 0.415 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.025 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".