A total of 50 participants were surveyed. Demographic data were available for 47 participants (94%). Complete data were available for the LAY, TAR, and EC groups (Table 1). In the HP group, data were missing for two participants and for one participant in the EP group. Of all study participants, 81% (n = 38) were male, and 19% (n = 9) were female. The mean age of all participants was 34 years (SD 9.08). The lowest mean age was 25 years in the LAY group, and the highest mean age was 43 years in the EP group. The mean professional experience was 8 years, with employees of TAR (12 years), EC (11 years), and EP (14 years) having a significantly longer average professional experience compared to HP (3 years).

Table 1 Demographic parameters of the surveyed participants (gender, age, and professional experience)Processing time per case

The average processing time per case was 68 s, with case 1 taking 96 s (ranging from 57 to 140 s) and the last processed case, case 22, taking 60 s (ranging from 37 to 98 s). The median processing time for the 10 non-complex cases before training (cases 1–10) was 73 s, and after training (cases 13–22), it was 59 s (p = 0.003, two-way ANOVA).

Figure 2 shows the decreasing trend in processing time with the number of cases performed. The complex cases required longer processing times. Cases 11 and 12 (before training) were completed on average after 81 s (ranging from 49 to 119 s). Cases 23 and 24 (after training) took an average of 72 s (ranging from 38 to 118 s).

Fig. 2

Processing time per case (mean and standard deviation). A trend of reduced processing time with case progression is evident, with the complex cases 11, 12, and 23, 24 (boxes) standing out as outliers. Cases before training (1–10) were significantly processed more slowly than after training (13–22, *p = 0.003, two-way ANOVA)

Impact of user training

Before training, of all participants, regardless of professional group and impact direction, 629 of the binary answers (10.7%, median 37 per parameter) were answered incorrectly. After training, this reduced to 340 answers (5.5%, median 11 per parameter). The sum of the differences between estimated and actual EES before training was 4250 km/h and decreased by 28.8% to 1223 km/h after training.

Regarding the quality of the input parameter assessments before vs. after training, regardless of impact direction, the following relative distributions were observed: vehicle class, 74% correct answers before and 74% after training; rigid obstacle collision, 90% before and 99% after training; rollover, 98% before and 98% after training; impact side, 91% before and 93% after training; damage to the passenger compartment, 71% before and 95% after training; EES within ± 10 km/h, 52% before and 74% after training; seatbelt usage, 97% before and 98% after training; curtain airbag, 88% before and 93% after training; front airbag, 96% before and 99% after training; knee airbag, 93% before and 94% after training; and side airbag, 80% before and 91% after training. Seat position, age group, seatbelt usage, and gender were predetermined for the participants (Table 2).

Table 2 Correct assessments by professional group, input parameters before and after user training, and the difference, with a breakdown by impact direction (relative distribution in %)All impact directions

Regardless of the impact direction, after training, LAY (− 6%), TAR, and hospital doctors (− 2% each) showed slightly poorer assessments of the vehicle class (not significant, p > 0.315). Significant improvements were observed in the assessment of a rigid obstacle collision by LAY (+ 6%, p = 0.034) and hospital doctors (+ 22%, p = 0.01), with a significant overall improvement among all professional groups (+ 8.4%, p < 0.01). After training, all professional groups exhibited significantly better assessments of passenger compartment damage (+ 20%, p < 0.007). The estimation of EES was also significantly better for EC (+ 30%, p = 0.009), hospital doctors (+ 35%, p = 0.019), EP (+ 35%, p = 0.011), and in the overall assessment (+ 22%, p < 0.001). In terms of assessing airbag deployments, significantly better results were obtained for the assessment of front airbag deployment by LAY (+ 8%, p = 0.02), as well as the assessment of side airbag deployment by LAY (+ 17%, p = 0.048), hospital doctors (+ 11%, p = 0.09), and EP (+ 10%, p = 0.04).

Frontal impact

Looking at the cases after a frontal collision, in addition to the overall assessment mentioned earlier, significant differences were observed in the assessment of impact direction by LAY (p = 0.023), EC (p = 0.008), and hospital doctors (p = 0.02). Laypersons also showed a significantly better estimation of the EES value (p = 0.024). Compared to the overall assessment (Table 2), differences in front and side airbag assessments by LAY were not significant.

Side impact

For side impact, after training, EC showed a significantly better assessment of rigid obstacle collision (p = 0.046), impact direction (p = 0.02), and side airbag (p = 0.03). Hospital doctors (p = 0.046) and EP (p = 0.025) also had better scores for impact direction. Compared to the overall assessment (Table 2), LAY did not show significant differences in the assessment of rigid obstacle collision, front and side airbags. For TAR, knee airbag, and EP, side airbag assessments were not significant.

Complex cases

Compared to the overall assessment (Table 2), LAY showed significantly better results after training for impact direction (p = 0.023), EES (p = 0.025), seatbelt usage (p = 0.008), and curtain airbag (p = 0.034). The significance for front and side airbags was not observed. TAR did not show significant differences, but EC showed significant differences in impact direction (p = 0.014) and curtain, front, and knee airbags (p = 0.046), with EES not being significant. Hospital doctors showed significant differences in rollover (p = 0.038) and impact direction (p = 0.005), while passenger compartment, EES, and side airbag assessments were no longer significant. Emergency physicians performed significantly better in assessing impact direction (p = 0.024) and front airbag (p = 0.025), with differences in EES and side airbag no longer being significant.

Interdisciplinary comparison before and after user training

The following analysis pertains to the 20 non-complex cases. When considering differences in the assessed input parameters before training, significant differences between professional groups are observed in the assessment of vehicle class (p = 0.047), rigid obstacle collision (p = 0.036), impact side (p = 0.032), EES (p = 0.006), seatbelt usage (p = 0.024), front airbag (p = 0.01), and side airbag (p = 0.017). Of these, only the assessment of front airbag remains as the sole significant difference after training (p = 0.014; Table 3).

Table 3 Comparison between input parameters before and after user trainingInterdisciplinary differences before training

In the further differentiation of the significantly differing input parameters, the interdisciplinary differences before training are presented in Table 4. Regarding the assessment of the vehicle class, TAR significantly outperformed LAY (p = 0.023) and EC. For rigid obstacle collision, hospital doctors performed significantly worse than LAY (p = 0.023), TAR (p = 0.007), and EP (p = 0.043). Impact side was rated significantly better by TAR compared to hospital doctors (p = 0.011). The EES was also significantly better estimated by TAR than by LAY (p = 0.009), EC (p = 0.005), hospital doctors (p = 0.001), and EP (p = 0.011). Seatbelt usage was predetermined; however, EP outperformed EC (p = 0.023). Concerning the front airbag, LAY were inferior to TAR (p = 0.023) and EP (p = 0.043). Side airbag was significantly better assessed by TAR compared to LAY (p = 0.019), EC (p = 0.003), and hospital doctors (p = 0.019).

Table 4 Interdisciplinary differences before user training, subdivided by input parameters (which showed significances in the Kruskal–Wallis test, Mann–Whitney U test)Interdisciplinary differences after training

For the assessment of the front airbag (Table 3), no significant interdisciplinary difference dependent on training was observed in the Mann–Whitney U test (p ≥ 0.28).