METHODS: The datasets were analysed using SPSS (version 26) and ChatGPT-4. Statistical tests included the independent t-test, paired t-test, ANOVA, chi-square test, Wilcoxon signed-rank test, Mann-Whitney U test, Pearson and Spearman correlation, regression analysis, kappa statistic, intraclass correlation coefficient (ICC), Bland-Altman analysis, and sensitivity and specificity analysis. Descriptive statistics were used to report results, and differences between the two tools were noted.
RESULTS: SPSS and ChatGPT-4 produced identical results for the independent sample t-test, paired t-test, and simple linear regression. In one-way ANOVA, both tools provided consistent F-values, but post-hoc analysis revealed discrepancies in mean differences and confidence intervals. Pearson chi-square and Wilcoxon signed-rank tests showed variations in p-values and Z-values. Mann-Whitney U test had differences in interquartile range (IQR), U, and Z-values. Pearson and Spearman's correlations were consistent, with IQR differences in Spearman. Sensitivity, specificity, and area under the curve (AUC) analyses were consistent, though differences in standard errors and confidence intervals were observed.
CONCLUSION: ChatGPT-4 produced accurate results for several statistical tests, matching SPSS in simpler analyses. However, discrepancies in post-hoc analyses, confidence intervals, and more complex tests indicate that careful validation is required when using ChatGPT-4 for detailed statistical work. Researchers should exercise caution and cross-validate results with established tools such as SPSS.
METHODS: This was a cross-sectional study. An online self-administered questionnaire was distributed to Malaysian citizens aged 18-37 years. The questionnaire consisted of 11 questions that investigated their awareness of non-dentists offering orthodontic treatment, the harmful effects of braces fitted by non-dentists, and potential strategies to mitigate this phenomenon.
RESULTS: The study was completed by 426 participants, predominantly Malay, with a mean age of 22.9 years. A total of 76.1% reported awareness of braces fixed by non-dentists, primarily through social media platforms such as Instagram and Facebook. Lower cost emerged as the predominant motive (83.6%) for opting for non-dentist orthodontic treatment, followed by no waiting list (48.8%). Notably, the majority of participants acknowledged the illegality (70%) and potential harm (77%) associated with non-dentists providing orthodontic treatment. Legal enforcement (53.1%) was identified as the preferred method for mitigating this practice. Occupation significantly influenced knowledge of illegal orthodontic treatment (p 0.05).
CONCLUSION: The survey revealed that young adults are aware of and informed about non-dentists offering orthodontic treatment. While they identified cost as the primary reason for seeking such services, they also recognized legislation and public awareness through campaigns and social media as effective strategies to address this issue. Additionally, significant differences in legal awareness were observed among different occupational levels.