RESUMO
BACKGROUND: Patients are increasingly using Generative Pre-trained Transformer 4 (GPT-4) to better understand their own radiology findings. PURPOSE: To evaluate the performance of GPT-4 in transforming cardiovascular magnetic resonance (CMR) reports into text that is comprehensible to medical laypersons. METHODS: ChatGPT with GPT-4 architecture was used to generate three different explained versions of 20 various CMR reports (n = 60) using the same prompt: "Explain the radiology report in a language understandable to a medical layperson". Two cardiovascular radiologists evaluated understandability, factual correctness, completeness of relevant findings, and lack of potential harm, while 13 medical laypersons evaluated the understandability of the original and the GPT-4 reports on a Likert scale (1 "strongly disagree", 5 "strongly agree"). Readability was measured using the Automated Readability Index (ARI). Linear mixed-effects models (values given as median [interquartile range]) and intraclass correlation coefficient (ICC) were used for statistical analysis. RESULTS: GPT-4 reports were generated on average in 52 s ± 13. GPT-4 reports achieved a lower ARI score (10 [9-12] vs 5 [4-6]; p < 0.001) and were subjectively easier to understand for laypersons than original reports (1 [1] vs 4 [4,5]; p < 0.001). Eighteen out of 20 (90%) standard CMR reports and 2/60 (3%) GPT-generated reports had an ARI score corresponding to the 8th grade level or higher. Radiologists' ratings of the GPT-4 reports reached high levels for correctness (5 [4, 5]), completeness (5 [5]), and lack of potential harm (5 [5]); with "strong agreement" for factual correctness in 94% (113/120) and completeness of relevant findings in 81% (97/120) of reports. Test-retest agreement for layperson understandability ratings between the three simplified reports generated from the same original report was substantial (ICC: 0.62; p < 0.001). Interrater agreement between radiologists was almost perfect for lack of potential harm (ICC: 0.93, p < 0.001) and moderate to substantial for completeness (ICC: 0.76, p < 0.001) and factual correctness (ICC: 0.55, p < 0.001). CONCLUSION: GPT-4 can reliably transform complex CMR reports into more understandable, layperson-friendly language while largely maintaining factual correctness and completeness, and can thus help convey patient-relevant radiology information in an easy-to-understand manner.
RESUMO
OBJECTIVES: To compare the mechanical properties of different layers of multi-layered zirconia materials. METHODS: 720 cylindric test plates were fabricated from four defined layers of three multi-layered zirconia ceramics (IPS e.max ZirCAD Prime, Optimill Multilayer 3D; Ceramill zolid fx multilayer) and divided into two equal groups. One group underwent thermal cycling (5-55 °C, 10 000 cycles; "TC") and one did not ("no TC"), before density, flexural strength, Weibull modulus, and Vickers hardness were evaluated. EDX analysis was conducted using an additional cylinder of each material. Data were analyzed using Kruskal-Wallis and Mann-Whitney U tests. Statistical analysis was performed with Bonferroni correction (α < 0.001). RESULTS: After aging, ZirCAD layer 4 showed the overall highest density (6.04 ± 0.02 g/cm3), which was significantly higher than density of layer 4 of Optimill (6.02 ± 0.06 g/cm3) and Ceramill (5.80 ± 1.08 g/cm3) (both p < 0.001). Flexural strength of ZirCAD and Optimill increased consecutively after thermal aging. ZirCAD layer 4 had the overall highest flexural strength before and after artificial aging. After thermal cycling, the Weibull modulus ranged between 4.32 (ZirCAD layer 1) and 13.58 (Ceramill layer 4). ZirCAD had the overall highest Vickers hardness: in layer 1 (1579.18 ± 47.14 HV) before aging, and in layer 2 (1607.1 ± 149.71 HV) after aging. Flexural strength and Vickers hardness differed significantly between the four ZirCAD layers (p < 0.001). Thermal ageing had no significant impact on mechanical properties (p > 0.001). SIGNIFICANCE: Mechanical properties were affected by plate position within the blank. When nesting a restoration within a multi-layered zirconia blank, the mechanical properties required should be considered.
