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The 2 + 1 SU(3) Polyakov linear sigma model is used to investigate the respective influence of a finite volume and a magnetic field on the quark-hadron phase boundary in the plane of baryon chemical potential (${\mu }_{B}$) versus temperature (T) of the quantum chromodynamics (QCD) phase diagram. The calculated results indicate sizable shifts of the quark-hadron phase boundary to lower values of $({\mu }_{B}\,\mathrm{and}\,T)$ for increasing magnetic field strength, and an opposite shift to higher values of $({\mu }_{B}\,\mathrm{and}\,T)$ for decreasing system volume. Such shifts could have important implications for the extraction of the thermodynamic properties of the QCD phase diagram from heavy ion data.

Pain catastrophizing is an exaggerated cognitive-affective response to actual or anticipated pain, usually measured by the Pain Catastrophizing Scale (PCS). Our study aimed to test the bifactor measurement model of the Hungarian PCS and to identify a catastrophizing risk group with a clinically meaningful cut-off score. The data of 404 chronic spine-related (neck, back and low-back) pain patients (mean age: 58.61 (SD = 14.34)) were used in our cross-sectional study. Besides pain-related and demographic data, pain catastrophizing and depressive symptoms were measured with questionnaires. Confirmatory factor analyses confirmed that the bifactor model outperformed the other tested measurement models, and the general catastrophizing factor was responsible for 81.5% of the explained variance. Using latent class analysis, we found that even moderately elevated pain catastrophizing score was related to more depressive symptoms and higher perceived pain intensity, and 22 score could be used as a cut-off score. Our results support the concept of global pain catastrophizing and the validity of the Hungarian PCS. Further studies are needed to evaluate the bifactor structure of this scale and the predictive value of the proposed cut-off score. Pain catastrophizing, Clinical interpretation, Psychometrics, Bifactor model, Confirmatory factor analysis.