Pregnancy-related breast cancer: 14-year experience in a tertiary institution in Hong Kong.

BACKGROUND: The incidence of pregnancy-associated breast cancer (PABC) is increasing. Its tumor characteristics and overall survival compared with those in nonpregnant patients remain controversial. While there have been suggestions that PABC patients have a 40 % increase in the risk of death compared to non-pregnant patients, other studies suggested similar disease outcomes. This study aims to review our local experience with PABC. METHODS: Twenty-eight patients diagnosed with PABC and twenty-eight patients diagnosed at premenopausal age randomly selected by a computer-generated system during the same period were recruited. Background characteristics, tumor features, and survival were compared. RESULTS: Among the twenty-eight pregnant patients, seventeen were diagnosed during pregnancy, and eleven were diagnosed in the postpartum period. Compared to the non-pregnant breast cancer patients, they presented with less progesterone receptor-positive tumor (35.7 % vs. 64.2 %, p = 0.03). Although there was no statistically significant difference in tumor size (p = 0.44) and nodal status (p = 0.16), the tumor tended to be larger in size (2.94 +/- 1.82 vs 2.40 +/- 1.69 cm) and with more nodal involvement (35.7 % vs 25.0 %). There was also a trend of delayed presentation to medical attention, with a mean duration of 13.1 weeks in the PABC group and 8.6 weeks in the control group. However, the overall survival did not differ (p = 0.63). CONCLUSION: PABC is increasing in incidence. They tend to have more aggressive features, but overall survival remains similar. A multidisciplinary approach is beneficial for providing the most appropriate care.

ChatGPT versus human in generating medical graduate exam multiple choice questions-A multinational prospective study (Hong Kong S.A.R., Singapore, Ireland, and the United Kingdom).

INTRODUCTION: Large language models, in particular ChatGPT, have showcased remarkable language processing capabilities. Given the substantial workload of university medical staff, this study aims to assess the quality of multiple-choice questions (MCQs) produced by ChatGPT for use in graduate medical examinations, compared to questions written by university professoriate staffs based on standard medical textbooks. METHODS: 50 MCQs were generated by ChatGPT with reference to two standard undergraduate medical textbooks (Harrison's, and Bailey & Love's). Another 50 MCQs were drafted by two university professoriate staff using the same medical textbooks. All 100 MCQ were individually numbered, randomized and sent to five independent international assessors for MCQ quality assessment using a standardized assessment score on five assessment domains, namely, appropriateness of the question, clarity and specificity, relevance, discriminative power of alternatives, and suitability for medical graduate examination. RESULTS: The total time required for ChatGPT to create the 50 questions was 20 minutes 25 seconds, while it took two human examiners a total of 211 minutes 33 seconds to draft the 50 questions. When a comparison of the mean score was made between the questions constructed by A.I. with those drafted by humans, only in the relevance domain that the A.I. was inferior to humans (A.I.: 7.56 +/- 0.94 vs human: 7.88 +/- 0.52; p = 0.04). There was no significant difference in question quality between questions drafted by A.I. versus humans, in the total assessment score as well as in other domains. Questions generated by A.I. yielded a wider range of scores, while those created by humans were consistent and within a narrower range. CONCLUSION: ChatGPT has the potential to generate comparable-quality MCQs for medical graduate examinations within a significantly shorter time.