Comparison of Unmanned Aerial Vehicle Technology versus Standard Practice of Scene Assessment by Paramedic Students of a Mass-Gathering Event
Bibliographic record
Abstract
INTRODUCTION: The proliferation of unmanned aerial vehicle (UAV) technology has the potential to change the situational awareness of medical incident commanders' (ICs') scene assessment of mass gatherings. Mass gatherings occur frequently and the potential for injury at these events is considered higher than the general population. These events have generated mass-casualty incidents (MCIs) in the past. The aim of this study was to compare UAV technology to standard practice (SP) in scene assessment using paramedic students during a mass-gathering event (MGE). METHODS: This study was conducted in two phases. Phase One consisted of validation of the videos and accompanying data collection tool. Phase One was completed by 11 experienced paramedics from a provincial Emergency Medical Services (EMS) service. Phase Two was a randomized comparison with 47 paramedic students from the Holland College Paramedicine Program (Charlottetown, Prince Edward Island, Canada) of the two scene assessment systems. For Phase Two, the paramedic students were randomized into a UAV or a SP group. The data collection tool consisted of two board categories: primary importance with 20 variables and secondary importance with 25 variables. After a brief narrative, participants were either shown UAV footage or the ground footage depending on their study group. After completion of the videos, study participants completed the data collection tool. RESULTS: The Phase One validation showed good consensus in answers to most questions (average 79%; range 55%-100%). For Phase Two, a Fisher's exact test was used to compare each variable from the UAV and SP groups using a P value of .05. Phase Two demonstrated a significant difference between the SP and UAV groups in four of 20 primary variables. Additionally, significant differences were found for seven out of 25 secondary variables. CONCLUSION: This study demonstrated the accurate, safe, and feasible use of a UAV as a tool for scene assessment by paramedic students at an MGE. No observed statistical difference was noted in a majority of both primary and secondary variables using a UAV for scene assessment versus SP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".