Table 1.

The fit results of random effect model A show that the noise, resolution, and dose are different at different facilities for both abdomen-pelvis and chest exams.

Outcome	Degrees freedom	Sum of squares	Mean square	$F$-value	$p$-value
Abdomen-pelvis
Log (noise)	94	3828.43	40.73	410.06	$<0.0001$
Log (dose)	94	2082.23	22.15	93.57	$<0.0001$
Resolution	92	39.63	0.43	64.13	$<0.0001$
Chest
Log (noise)	80	2055.84	25.7	65.94	$<0.0001$
Log (dose)	80	1870.7	23.38	75.88	$<0.0001$
Resolution	70	48.16	48.16	41.9	$<0.0001$

Specifically, the fit results here show that for each of the combinations of outcome and body part, the facility (the random intercept term) has a significant effect. The degrees of freedom denote the number of facilities in the dataset that are considered in each of the models. The missing resolution values are responsible for the lower degrees of freedom in the model. The sum of squares and mean square (sum of squares divided by the degrees of freedom) reflect variability in measurements in the dataset, higher values indicating higher variability. The $F$-value reflects the degree to which the facility-ID is correlated with noise, dose, or resolution. The high $F$-values, and low $p$-values together point to the fact that the noise, dose, and resolution all change significantly at different facilities. Observations with missing covariates or outcomes were dropped when fitting the models so that a subset of 97 facilities were reported.