The next nine minutes: Lessons learned from the large-scale active shooter training prior to the STEM school shooting
Bibliographic record
Abstract
OBJECTIVE: As the incidence of active shooters increase, local emergency response has also changed. South Metro Fire Rescue coordinated a series of hyper-realistic active shooter simulation drills involving multiple agencies. METHODS: "The Next Nine Minutes" was one of the largest active shooter drills performed to date with 904 personnel that were trained in 18 mass casualty active shooter drills. Evaluation was from point of injury to and including care in the operating room (OR), and evaluation of real-time system logistics. RESULTS: A total of 126 patients in Cut Suits® received a total of 479 procedures such as needle decompressions, cricothyrotomies, tourniquets, wound packs, and chest tubes. Central to this exercise, law enforcement (LE) established a warm zone from the initial shooting. EMS was able to move into the facility, locate casualties, extract the first victim, move them to a casualty collection point (CCP), and transport them to safety within 12 minutes. CONCLUSIONS: Strengths and weaknesses were identified in prehospital and in-hospital care. These included what roles agencies play in a true event, specific timing in establishing areas such as the warm zone and CCP, transportation, and logistics at the accepting hospitals. Only after the barriers to success were identified and addressed did the timing of casualty movement drastically improve. Lessons learned from this training were ultimately used to save lives at the STEM School, Highlands Ranch, and Colorado Shooting. This in situ immersion training should be practiced as a whole system.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".