A Qualitative Assessment of Studies Evaluating the Classification Accuracy of Personnel Using START in Disaster Triage: A Scoping Review
Bibliographic record
Abstract
Background: Mass casualty incidents (MCIs) can occur as a consequence of a wide variety of events and often require overwhelming prehospital and emergency support and coordinated emergency response. A variety of disaster triage systems have been developed to assist health care providers in making difficult choices with regards to prioritization of victim treatment. The simple triage and rapid treatment (START) triage system is one of the most widely used triage algorithms; however, the research literature addressing real-world or simulation studies documenting the classification accuracy of personnel using START is lacking. Aims and Objectives: To explore the existing literature related to the current state of knowledge about studies assessing the classification accuracy of the START triage system. Design: Scoping review based on Arksey and O'Malley's methodological framework and narrative synthesis based on methods described by Popay and colleagues were performed. Results: The literature search identified 1,820 citations, of which 32 studies met the inclusion criteria. Thirty were peer-reviewed articles and 28 published in the last 10 years (i.e., 2010 and onward). Primary research studies originated in 13 countries and included 3,706 participants conducting triaging assessments involving 2,950 victims. Included studies consisted of five randomized controlled trials, 17 non-randomized controlled studies, eight descriptive studies, and two mixed-method studies. Simulation techniques, mode of delivery, contextual features, and participants' required skills varied among studies. Overall, there was no consistent reporting of outcomes across studies and results were heterogeneous. Data were extracted from the included studies and categorized into two themes: (1) typology of simulations and (2) START system in MCIs simulations. Each theme contains sub-themes regarding the development of simulation employing START as a system for improving individuals' preparedness. These include types of simulation training, settings, and technologies. Other sub-themes include outcome measures and reference standards. Conclusion: This review demonstrates a variety of factors impacting the development and implementation of simulation to assess characteristics of the START system. To further improve simulation-based assessment of triage systems, we recommend the use of reporting guidelines specifically designed for health care simulation research. In particular, reporting of reference standards and test characteristics need to improve in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".