Background: The extended Consolidated Standards of Reporting Trials (CONSORT) Statement for Abstracts was developed to improve the quality of reports of randomized controlled trials (RCTs) because readers often base their assessment of a trial solely on the To date, few data exist regarding whether it has achieved this goal. We evaluated the extent of adherence to the CONSORT for Abstract statement for quality of reports on RCT abstracts by four high-impact general medical journals.Methods: A descriptive analysis of published RCT abstracts in The New England Journal of Medicine (NEJM), The Lancet, The Journal of American Medical Association (JAMA), and the British Medical Journal (BMJ) in the year 2010 was conducted by two reviewers, independently extracting data from a MEDLINE/PubMed search.Results: We identified 271 potential RCT abstracts meeting our inclusion criteria. More than half of the abstracts identified the study as randomized in the title (58.7%; 159/271), reported the specific objective/hypothesis (72.7%; 197/271), described participant eligibility criteria with settings for data collection (60.9%; 165/271), detailed the interventions for both groups (90.8%; 246/271), and clearly defined the primary outcome (94.8%; 257/271). However, the methodological quality domains were inadequately reported: allocation concealment (11.8%; 32/271) and details of blinding (21.0%; 57/271). Reporting the primary outcome results for each group was done in 84.1% (228/271). Almost all of the abstracts reported trial registration (99.3%; 269/271), whereas reports of funding and of harm or side effects from the interventions were found in only 47.6% (129/271) and 42.8% (116/271) of the abstracts, respectively.Conclusions: These findings show inconsistencies and non-adherence to the CONSORT for abstract guidelines, especially in the methodological quality domains. Improvements in the quality of RCT reports can be expected by adhering to existing standards and guidelines as expressed by the CONSORT group.

ChatGPT, a recently developed artificial intelligence (AI) chatbot, has demonstrated improved performance in examinations in the medical field. However, thus far, an overall evaluation of the potential of ChatGPT models (ChatGPT-3.5 and GPT-4) in a variety of national health licensing examinations is lacking. This study aimed to provide a comprehensive assessment of the ChatGPT models' performance in national licensing examinations for medical, pharmacy, dentistry, and nursing research through a meta-analysis. Following the PRISMA protocol, full-text articles from MEDLINE/PubMed, EMBASE, ERIC, Cochrane Library, Web of Science, and key journals were reviewed from the time of ChatGPT's introduction to February 27, 2024. Studies were eligible if they evaluated the performance of a ChatGPT model (ChatGPT-3.5 or GPT-4); related to national licensing examinations in the fields of medicine, pharmacy, dentistry, or nursing; involved multiple-choice questions; and provided data that enabled the calculation of effect size. Two reviewers independently completed data extraction, coding, and quality assessment. The JBI Critical Appraisal Tools were used to assess the quality of the selected articles. Overall effect size and 95% confidence intervals [CIs] were calculated using a random-effects model. A total of 23 studies were considered for this review, which evaluated the accuracy of four types of national licensing examinations. The selected articles were in the fields of medicine (n = 17), pharmacy (n = 3), nursing (n = 2), and dentistry (n = 1). They reported varying accuracy levels, ranging from 36 to 77% for ChatGPT-3.5 and 64.4-100% for GPT-4. The overall effect size for the percentage of accuracy was 70.1% (95% CI, 65-74.8%), which was statistically significant (p < 0.001). Subgroup analyses revealed that GPT-4 demonstrated significantly higher accuracy in providing correct responses than its earlier version, ChatGPT-3.5. Additionally, in the context of health licensing examinations, the ChatGPT models exhibited greater proficiency in the following order: pharmacy, medicine, dentistry, and nursing. However, the lack of a broader set of questions, including open-ended and scenario-based questions, and significant heterogeneity were limitations of this meta-analysis. This study sheds light on the accuracy of ChatGPT models in four national health licensing examinations across various countries and provides a practical basis and theoretical support for future research. Further studies are needed to explore their utilization in medical and health education by including a broader and more diverse range of questions, along with more advanced versions of AI chatbots.

Hwang SY, et al. J Korean Med Sci. 2023 Oct;38:e333. https://doi.org/10.3346/jkms.2023.38.e333

- Exposure to aerosolized Bacillus anthracis to decrease the incidence or progression of the disease (FDA licensed)