Background: Foramen magnum stenosis (FMS) is a serious complication in children with achondroplasia that may necessitate cervicomedullary decompression (CMD). It is unclear how FMS and CMD influence growth in these children. This study aimed to assess the effects of FMS and CMD on the growth of children with achondroplasia. Methods: Eighty-seven children (45 males, 42 females) with achondroplasia, aged 4 to 6 years, were evaluated. Height, weight, head circumference, and body mass index were expressed as standard deviation scores (SDS) according to Merker et al., while sitting height SDS was derived using Tanner’s methods. FMS was graded on magnetic resonance imaging using Fornarino’s score. Results: Fifty-two patients (26 males, 26 females) underwent CMD at a median age of 0.95 years (IQR 0.52;1.50). Of these, 28 (53.8%) were under one year old at the time of CMD, with a median age of 0.6 years (0.4;0.7). The remaining 24 children had CMD after their first year of life, with a median age of 1.6 years (1.3;2.8). The median age at anthropometric assessment was 5.16 years (4.74;5.50). Children who underwent CMD showed significantly lower median height SDS, particularly among males compared to females (p=0.026). Conclusions: Impaired growth in children with foramen magnum stenosis requiring cervicomedullary decompression may primarily reflect greater disease severity, while the potential contribution of surgery remains uncertain.

Background: The expanding interest of chatbots within the medical domain underscores the imperative for a comprehensive understanding of their capabilities and limitations, particularly in the context of anatomical education. Chatbots possess the potential to comprehend intricate anatomical concepts, deliver both advanced and contextually relevant information, and could serve as a valuable resource for medical students and educators. This study aimed to evaluate the proficiency and constraints of chatbots in the domain of neuroanatomy. Methods: We developed 30 questions and administered them to ChatGPT-4, Google Gemini, Microsoft Copilot, and Perplexity.ai, in their open versions. Questions were collaboratively constructed by the research team, selected through a semi-randomized process within the domain of neuroanatomy. Chatbots' responses were evaluated in a blinded manner for validity and appropriateness, utilizing a 5-point Likert scale. Results: The optimal performance was exhibited by ChatGPT-4 and Perplexity.ai, which achieved scores of 4.6 ± 0.5 and 4.5 ± 0.5, respectively. Microsoft Copilot (4.4 ± 0.5) and Google Gemini (4.1 ± 1.0) followed. The least successful performance was observed in the task of generating a neuroanatomical structure: only Microsoft Copilot attempted to fulfill the request, albeit with a dramatically flawed outcome. Conversely, Google Gemini and Perplexity.ai provided web links to anatomical illustrations. Conclusions: Despite technological advancements, AI models have not yet reached a level of sophistication sufficient to entirely supplant the role of educators or facilitators in a neuroanatomy course; however, they can serve as valuable adjunct tools for medical educators and students when utilized with careful consideration.

Background: The expanding interest in chatbots within the medical domain underscores the imperative for a comprehensive understanding of their capabilities and limitations, particularly in the context of anatomical education. Chatbots possess the potential to comprehend intricate anatomical concepts, deliver both advanced and contextually relevant information, and serve as a valuable resource for medical students and educators. This study aimed to evaluate the proficiency and constraints of chatbots in the domain of neuroanatomy. Methods: We developed 30 questions and administered them to ChatGPT-4, Google Gemini, Microsoft Copilot, and Perplexity.ai in their open versions. Questions were collaboratively constructed by the research team, selected through a semi-randomized process within the domain of neuroanatomy. Chatbots’ responses were evaluated in a blinded manner for validity and appropriateness, utilizing a 5-point Likert scale. Results: The highest observed performance among the evaluated chatbots was exhibited by ChatGPT-4 and Perplexity.ai, which achieved scores of 4.6 ± 0.5 and 4.5 ± 0.5, respectively. Microsoft Copilot (4.4 ± 0.5) and Google Gemini (4.1 ± 1.0) followed. The least successful performance was observed in the task of generating a neuroanatomical structure: only Microsoft Copilot attempted to fulfil the request, albeit with a dramatically flawed outcome. Conversely, Google Gemini and Perplexity.ai provided web links to anatomical illustrations. Conclusions: Despite technological advancements, AI models have not yet reached a level of sophistication sufficient to entirely supplant the role of educators or facilitators in a neuroanatomy course; however, they can serve as valuable adjunct tools for medical educators and students when utilized with careful consideration.

Background/Objectives. Pediatric brain tumor survivors (PBTS) are at increased risk of cardiometabolic complications due to tumor- and treatment-related hypothalamic–pituitary (HP) damage and adverse body composition. We evaluated associations of the Single-Point Insulin Sensitivity Estimator (SPISE) with central adiposity, DXA-derived body composition, metabolic abnormalities, and HP involvement. Methods. In this retrospective study, 119 PBTSs (median age, 12.7 years; median time since therapy, 2.4 years) were evaluated. SPISE, BMI, waist-to-height ratio (WHtR), DXA-derived fat-to-lean ratio (FLR), and metabolic syndrome (MetS) were assessed. Multivariable logistic regression evaluated the association between SPISE and central adiposity. Exploratory receiver operating characteristic analyses compared the discriminatory performance of SPISE, BMI-SDS, HOMA-IR, and FLR. Results. Central adiposity was present in 58.8% of participants, whereas BMI-defined obesity occurred in 21.8%. Lower SPISE values were associated with central adiposity, obesity, MetS, HP damage, and multiple pituitary hormone deficiencies (all p < 0.05). SPISE declined across pubertal stages and remained associated with central adiposity after adjustment for age and selected HP variables (OR = 0.58, 95% CI 0.46–0.73). SPISE showed good discrimination of central adiposity (AUC = 0.81), with an exploratory, cohort-derived threshold of 7.82; FLR provided complementary information on body composition. Conclusions. Central adiposity was common and not fully captured by BMI-defined obesity. Lower SPISE values were associated with concurrent anthropometric and metabolic abnormalities, while DXA-derived FLR added descriptive information on body composition. Whether these indices improve cardiometabolic risk stratification beyond conventional measures requires prospective investigation.