Page 93 - JSOM Summer 2023

FIGURE 3 Integrated debriefing of the computer-based simulation. mean scale score of 3.6 vs. 3.1 (p = .01), with better student
satisfaction concerning the global management of the MCI (3.9
vs. 3.5; p = .04). Cooperation with caregivers was self-reported
to be more effective in the control group (3.7 vs. 4.1; p = .04).

Discussion
TRAUMASIMS was effective in teaching MCI management
by integrating prehospital damage-control actions, categoriza-
tion of victims, and evacuation orders. Reducing preventable
deaths is a high priority of combat casualty care. Recent analy-
ses of modern conflicts advocated both the early evacuation of
those with life-threatening injuries and immediate application
of lifesaving interventions. 2,9,13 In the French concept of pro-
longed field care, military doctors could face MCIs in prehos-
TABLE 3 Assessment During Full-Scale Simulation pital settings, where limited resources place greater emphasis
4
on triage. TRAUMASIMS validated the main features of an
Study Control
FFCCC Benchmarks Group Group p-Value effective high-fidelity SG, including repetition of scenarios,
Massive bleeding (M) (% of success) 96.2 97.4 0.94 thereby gradually increasing difficulty by varying the num-
ber and severity of the wounded and providing individualized
Airway assessment (A) (% of success) 97.2 94.9 0.56 learning in a controlled virtual environment. 10,14
Respiration assessment (R)
(% of success) 96.4 87.1 0.002 Computer-based technologies, such as e-learning, online open
Circulation assessment (C)
(% of success) 96.4 87.1 0.06 courses, and immersive virtual reality or augmented reality,
Neurological assessment (H) 92.3 58.8 <0.001 have become increasingly prevalent in medical education,
(% of success) encouraged by a legal and ethical “not the first time on the
Prevention of hypothermia (H) 49.5 27.3 0.01 patient” policy, and have already proven to be beneficial com-
(% of success) pared with traditional text-based support in other educational
15–19
Analgesic administration 85.6 86.9 0.002 and military domains. Considering military medicine,
(% of success) virtual simulations have already been designed in TCCC pro-
Antibiotics administration 83.4 58 <0.001 grams for several military medical settings. 20–22 Compliance
(% of success) with TCCC guidelines has varied according to nation-specific
Appropriate recovery position 68.7 45.8 0.006 policies. 23–26 Therefore, in 2014, the FHMS considered the de-
(% of success) velopment of 3D-SC1 (three-dimensional Sauvetage au Com-
Evacuation (E) of the CCP bat-1), an innovative SG devoted to the training of soldiers
(% of success) 93.4 88 0.09 for casualty care under fire, officially published by the FMHS
CAT-A identification (1–5 scale)* 4.16 3.88 0.51 Academy (École du Val-de-Grâce) in 2007. 4,7,27,28 Following
Time to CAT-A evacuation this first experience of SG for FFCCC training, a military
(1–5 scale)* 4.1 3.8 0.28 version of TRAUMASIMS will contribute to the training of
Time to CAT-B/C evacuation combat lifesavers (SC2) and nurses and physicians (SC3) for
(1–5 scale)* 4.24 3.43 0.001 prolonged field care applications.
Overall error rate 11.9 23.4 <0.001
AE, absolute emergency; CCP, casualty collection point; FFCCC, TRAUMASIMS confronted the trainee with each casualty,
French Forward Combat Casualty Care; RE, relative emergency thereby instilling the reflex of making a standard assessment
*CAT, categorizing wounded-in-action using a three-level method: and an immediate lifesaving intervention. Debriefing focused
CAT A = Urgent, B = Priority, C = Routine
on these nontechnical medical skills. FFCCC completion and
categorization suggested that the improvement in triage skills
Table 2 shows the results concerning the categorization of could be attributed to TRAUMASIMS. Performance indicators
emergencies and the evacuation of victims. The identifica- in simulation-based procedural education were traditionally
tion of CAT A, Urgent casualties, was comparable in the two divided into objective and subjective indicators, with a good
groups (p = .51), as was the time taken to evacuate them correlation between these two types. 10,14 Objective indicators,
(p = .28). However, there was a significant difference in the such as error rate in FFCCC implementation and evacuation
evacuation time of relative emergencies (CAT B/C, Priority/ time of CCP, advocated for the effectiveness of SG-based teach-
Routine): this type of emergency was evacuated more quickly ing regarding the “golden hour” policy. Subjective indicators,
22
in the study group than in the control group (p = .001). such as a scenario-dependent order to evacuate, described as
an essential outcome in an evolving tactical environment, sug-
Concerning basal stress levels evaluated using the Basal State– gested a benefit of SG-based training.
Trait Anxiety Inventory, there was no difference between the
groups (47 vs. 47.1; p = .61). Just before the STX, the level of Aside from SG, STX are described as the “gold standard” for
acute anxiety (Acute State–Trait Anxiety Inventory) was equiv- pre-deployment training, but their organization is confronted
alent in the two groups (p = .84). Directly after the simulation, with several limitations: they are costly and time consuming
acute anxiety levels were not statistically different between the and may disrupt local services. In civilian practice, on-scene
groups (p = .94). Using self-debriefing after the STX, the study and in-hospital triage have been identified as weaknesses
group reported feeling less stress during the exercise, with a during the recent French terrorist attacks, and STX have been

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