Purpose: Pediatric papillary thyroid cancer (PTC) is recommended to perform aggressive surgery to reduce the risk of recurrence.This study was designed to evaluate the concurrent association between multifocality, bilaterality, and the risk of recurrence in pediatric PTC. Materials and Methods: This retrospective cohort study included pediatric patients (age <19 years) who underwent total thyroidectomy for PTC between 1996 and 2014 in a single tertiary center. Clinicopathological parameters were analyzed to evaluate the prevalence of multifocality, bilaterality, recurrence, and their association. Results: We analyzed 58 pediatric patients with PTC. There was no factor related to the presence of multifocality or bilaterality in multivariate analysis. Also, in univariate analysis, multifocality and bilaterality were not independent risk factors of each other’s presentation (p=0.061 and p=0.061, respectively). Recurrence was observed in 19 (32.8%) patients. In multivariate analysis of recurrence, clear cell subtype, multifocality, and gross extrathyroidal extension (ETE) were independent risk factors (p=0.027, p=0.035, and p=0.038, respectively). Most recurrences (68.4%) happened during the first 4 years of follow-up after the initial thyroidectomy. Conclusion Multifocality and bilaterality were not independent risk factors of each other’s presentation; however, multifocality was the risk factor for recurrence in pediatric PTC. For pediatric PTC, close monitoring for recurrence within the initial 4 years is recommended, particularly in patients with clear cell subtype, multifocality, and gross ETE.

Purpose: Pediatric papillary thyroid cancer (PTC) is recommended to perform aggressive surgery to reduce the risk of recurrence.This study was designed to evaluate the concurrent association between multifocality, bilaterality, and the risk of recurrence in pediatric PTC. Materials and Methods: This retrospective cohort study included pediatric patients (age <19 years) who underwent total thyroidectomy for PTC between 1996 and 2014 in a single tertiary center. Clinicopathological parameters were analyzed to evaluate the prevalence of multifocality, bilaterality, recurrence, and their association. Results: We analyzed 58 pediatric patients with PTC. There was no factor related to the presence of multifocality or bilaterality in multivariate analysis. Also, in univariate analysis, multifocality and bilaterality were not independent risk factors of each other’s presentation (p=0.061 and p=0.061, respectively). Recurrence was observed in 19 (32.8%) patients. In multivariate analysis of recurrence, clear cell subtype, multifocality, and gross extrathyroidal extension (ETE) were independent risk factors (p=0.027, p=0.035, and p=0.038, respectively). Most recurrences (68.4%) happened during the first 4 years of follow-up after the initial thyroidectomy. Conclusion Multifocality and bilaterality were not independent risk factors of each other’s presentation; however, multifocality was the risk factor for recurrence in pediatric PTC. For pediatric PTC, close monitoring for recurrence within the initial 4 years is recommended, particularly in patients with clear cell subtype, multifocality, and gross ETE.

Purpose: Pediatric papillary thyroid cancer (PTC) is recommended to perform aggressive surgery to reduce the risk of recurrence.This study was designed to evaluate the concurrent association between multifocality, bilaterality, and the risk of recurrence in pediatric PTC. Materials and Methods: This retrospective cohort study included pediatric patients (age <19 years) who underwent total thyroidectomy for PTC between 1996 and 2014 in a single tertiary center. Clinicopathological parameters were analyzed to evaluate the prevalence of multifocality, bilaterality, recurrence, and their association. Results: We analyzed 58 pediatric patients with PTC. There was no factor related to the presence of multifocality or bilaterality in multivariate analysis. Also, in univariate analysis, multifocality and bilaterality were not independent risk factors of each other’s presentation (p=0.061 and p=0.061, respectively). Recurrence was observed in 19 (32.8%) patients. In multivariate analysis of recurrence, clear cell subtype, multifocality, and gross extrathyroidal extension (ETE) were independent risk factors (p=0.027, p=0.035, and p=0.038, respectively). Most recurrences (68.4%) happened during the first 4 years of follow-up after the initial thyroidectomy. Conclusion Multifocality and bilaterality were not independent risk factors of each other’s presentation; however, multifocality was the risk factor for recurrence in pediatric PTC. For pediatric PTC, close monitoring for recurrence within the initial 4 years is recommended, particularly in patients with clear cell subtype, multifocality, and gross ETE.

Purpose: Pediatric papillary thyroid cancer (PTC) is recommended to perform aggressive surgery to reduce the risk of recurrence.This study was designed to evaluate the concurrent association between multifocality, bilaterality, and the risk of recurrence in pediatric PTC. Materials and Methods: This retrospective cohort study included pediatric patients (age <19 years) who underwent total thyroidectomy for PTC between 1996 and 2014 in a single tertiary center. Clinicopathological parameters were analyzed to evaluate the prevalence of multifocality, bilaterality, recurrence, and their association. Results: We analyzed 58 pediatric patients with PTC. There was no factor related to the presence of multifocality or bilaterality in multivariate analysis. Also, in univariate analysis, multifocality and bilaterality were not independent risk factors of each other’s presentation (p=0.061 and p=0.061, respectively). Recurrence was observed in 19 (32.8%) patients. In multivariate analysis of recurrence, clear cell subtype, multifocality, and gross extrathyroidal extension (ETE) were independent risk factors (p=0.027, p=0.035, and p=0.038, respectively). Most recurrences (68.4%) happened during the first 4 years of follow-up after the initial thyroidectomy. Conclusion Multifocality and bilaterality were not independent risk factors of each other’s presentation; however, multifocality was the risk factor for recurrence in pediatric PTC. For pediatric PTC, close monitoring for recurrence within the initial 4 years is recommended, particularly in patients with clear cell subtype, multifocality, and gross ETE.

Purpose: Pediatric papillary thyroid cancer (PTC) is recommended to perform aggressive surgery to reduce the risk of recurrence.This study was designed to evaluate the concurrent association between multifocality, bilaterality, and the risk of recurrence in pediatric PTC. Materials and Methods: This retrospective cohort study included pediatric patients (age <19 years) who underwent total thyroidectomy for PTC between 1996 and 2014 in a single tertiary center. Clinicopathological parameters were analyzed to evaluate the prevalence of multifocality, bilaterality, recurrence, and their association. Results: We analyzed 58 pediatric patients with PTC. There was no factor related to the presence of multifocality or bilaterality in multivariate analysis. Also, in univariate analysis, multifocality and bilaterality were not independent risk factors of each other’s presentation (p=0.061 and p=0.061, respectively). Recurrence was observed in 19 (32.8%) patients. In multivariate analysis of recurrence, clear cell subtype, multifocality, and gross extrathyroidal extension (ETE) were independent risk factors (p=0.027, p=0.035, and p=0.038, respectively). Most recurrences (68.4%) happened during the first 4 years of follow-up after the initial thyroidectomy. Conclusion Multifocality and bilaterality were not independent risk factors of each other’s presentation; however, multifocality was the risk factor for recurrence in pediatric PTC. For pediatric PTC, close monitoring for recurrence within the initial 4 years is recommended, particularly in patients with clear cell subtype, multifocality, and gross ETE.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.