Methods: Thirty-two patients undergoing elective AH were randomised into Group ITM (ITM 0.2 mg + 2.5 mL 0.5% bupivacaine) (n = 16) and Group EB (0.25% bupivacaine bolus + continuous infusion of 0.1% bupivacaine-fentanyl 2 μg/mL) (n = 16).The procedure was performed before induction, and all patients subsequently received standard general anaesthesia. Both groups were provided patient-controlled analgaesia morphine (PCAM) as a backup. Visual analogue scale (VAS) scores, total morphine consumption, hospital stay duration, early mobilisation time and first PCAM demand time were recorded.
Results: The median VAS score was lower for ITM than for EB after the 1st hour [1.0 (IqR 1.0) versus 3.0 (IqR 3.0), P < 0.001], 8th hour [1.0 (IqR 1.0) versus 2.0 (IqR 1.0), P = 0.018] and 16th hour [1.0 (IqR1.0) versus (1.0 (IqR 1.0), P = 0.006]. The mean VAS score at the 4th hour was also lower for ITM [1.8 (SD 1.2) versus 2.9 (SD 1.4), P = 0.027]. Total morphine consumption [11.3 (SD 6.6) versus 16.5 (SD 4.8) mg, P = 0.016] and early mobilisation time [2.1 (SD 0.3) versus 2.6 (SD 0.9) days, P = 0.025] were also less for ITM. No significant differences were noted for other assessments.
Conclusions: The VAS score was better for ITM than for EB at earlier hours after surgery. However, in terms of acceptable analgaesia (VAS ≤ 3), both techniques were comparable over 24 hours.
OBJECTIVE: This study aims to examine the potential for, and concerns of, using AI in scientific research. For this purpose, high-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application's impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text.
METHODS: A total of 4 articles were generated using ChatGPT, and thereafter evaluated by 23 reviewers. The researchers developed an evaluation form to assess the quality of the articles generated. Additionally, 50 abstracts were generated using ChatGPT and their quality was evaluated. The data were subjected to ANOVA and thematic analysis to analyze the qualitative data provided by the reviewers.
RESULTS: When using detailed prompts and providing the context of the study, ChatGPT would generate high-quality research that could be published in high-impact journals. However, ChatGPT had a minor impact on developing the research framework and data analysis. The primary area needing improvement was the development of the literature review. Moreover, reviewers expressed concerns around ownership and the integrity of the research when using AI-generated text. Nonetheless, ChatGPT has a strong potential to increase human productivity in research and can be used in academic writing.
CONCLUSIONS: AI-generated text has the potential to improve the quality of high-impact research articles. The findings of this study suggest that decision makers and researchers should focus more on the methodology part of the research, which includes research design, developing research tools, and analyzing data in depth, to draw strong theoretical and practical implications, thereby establishing a revolution in scientific research in the era of AI. The practical implications of this study can be used in different fields such as medical education to deliver materials to develop the basic competencies for both medicine students and faculty members.