Search Results (4)

Background/Objectives: Large-language modules facilitate accessing health information instantaneously. However, they do not provide the same level of accuracy or detail. In pediatric orthopedics, where parents have urgent concerns regarding knee deformities (bowlegs and knock knees), the accuracy and dependability of these chatbots can affect parent decisions to seek treatment. The goal of this study was to analyze how AI chatbots addressed parental concerns regarding pediatric knee deformities. Methods: A set of twenty standardized questions, consisting of ten questions each on bowlegs and knock knees, were designed through literature reviews and through analysis of parental discussion forums and expert consultations. Each of the three chatbots (ChatGPT, Gemini, and Copilot) was asked the same set of questions. Five pediatric orthopedic surgeons were then asked to rate each response for accuracy, clarity, and comprehensiveness, along with the degree of misleading information provided, on a scale of 1–5. The reliability among raters was calculated using intraclass correlation coefficients (ICCs), while differences among the chatbots were assessed using a Kruskal–Wallis test with post hoc pairwise comparisons. Results: All three chatbots displayed a moderate-to-good score for inter-rater reliability. ChatGPT and Gemini’s scores were higher for accuracy and comprehensiveness than Copilot’s (p < 0.05). However, no notable differences were found in clarity or in the likelihood of giving incorrect answers. Overall, more detailed and precise responses were given by ChatGPT and Gemini, while, with regard to clarity, Copilot performed comparably but was less thorough. Conclusions: There were notable discrepancies in performance across the AI chatbots in providing pediatric orthopedic information, which demonstrates indications of evolving potential. In comparison to Copilot, ChatGPT and Gemini were relatively more accurate and comprehensive. These results highlight the persistent requirement for real-time supervision and stringent validation when employing chatbots in the context of pediatric healthcare. Full article

Background: Football, as the most popular sport worldwide, has long been under suspicion of causing varus knee alignment as early as adolescence. However, no causal relationship has yet been found. The first step to do so would be to determine the prevalence of lower leg malalignment among male junior football players depending on age, performance level and the number of active seasons played. Methods: Leg axis alignment in frontal plane was determined in male junior football players of different age levels between 7 and 18 years by measuring the intercondylar/intermalleolar distance (ICD/IMD) in an upright position. In addition to anthropometric data, multiple sport-specific data such as the start of their football career or training time per week were collected by means of questionnaires (clinical trial registration number: DRKS00020446). Results: 207 male junior football players were included in this survey. The mean age was 12.8 years. Within the group of 15 to 18 year olds, the prevalence of varus knee malalignment was highest at just under a third (32.1%). In the subpopulation that played actively for more than ten seasons, 28.9% showed varus leg axis. Regarding performance level, the highest IMD/ICD values and highest prevalence of varus alignment were found among football players who play on a semi-professional level (16.7%), compared to amateur (11.4%) and high-performance levels (2.8%). Conclusions: Further research is necessary to investigate if this high prevalence of varus knee in children of a higher age and higher playing levels is due to the selection bias of football players with varus knee or a real development of varus knee in individuals. Full article

The prevalence of varus knee malalignment among junior and adult football players (FP) has proven to be higher compared to other sports. No causal relationship has yet been found, as genu varum can be assumed to be an independent risk factor for the development of knee osteoarthritis. The purpose of this study is to compare knee alignment measurements and sport-specific data of adolescent football players and referees (REF). Knee alignment was detected by measuring the intercondylar/intermalleolar distance (ICD/IMD) as well as the Hip–Knee–Ankle angle (HKA) using a standardized digital frontal-plane photograph. Anthropometric and sports-related data (training/match exposure, seasons actively played, etc.) were collected by means of questionnaires (Clinical trial registration number: DRKS00020446). A total of 28 male FP and 29 male adolescent REF were included in the survey. The mean age was 17.4 ± 0.7 years. The two groups did not differ significantly in age, height, weight, BMI, and overall football/refereeing exposure per week (FP vs. REF: 274 vs. 285 min/week, p = 0.61). The HKA of the FP was significantly lower (toward varus) than that of the REF (177.6° ± 2.4° vs. 179.0° ± 2.4°; p < 0.001). However, ICD did not significantly differ (FP: 17 ± 25 mm, REF: 13 ± 27 mm; p = 0.55). The football environment with frequent football exposure seems to have an influence on leg axis deviation in FP compared to REF. For prevention of knee osteoarthritis in FP, an advanced understanding of leg axis development in adolescent players is essential and, therefore, needs further research. Full article

Early identification of pathological causes for pediatric genu varum (bowlegs) is crucial for preventing a progressive, irreversible knee deformity of the child. This study aims to develop and validate a diagnostic clinical prediction algorithm for assisting physicians in distinguishing an early stage of Blount’s disease from the physiologic bowlegs to provide an early treatment that could prevent the progressive, irreversible deformity. The diagnostic prediction model for differentiating an early stage of Blount’s disease from the physiologic bowlegs was developed under a retrospective case-control study from 2000 to 2017. Stepwise backward elimination of multivariable logistic regression modeling was used to derive a diagnostic model. A total of 158 limbs from 79 patients were included. Of those, 84 limbs (53.2%) were diagnosed as Blount’s disease. The final model that included age, BMI, MDA, and MMB showed excellent performance (area under the receiver operating characteristic (AuROC) curve: 0.85, 95% confidence interval 0.79 to 0.91) with good calibration. The proposed diagnostic prediction model for discriminating an early stage of Blount’s disease from physiologic bowlegs showed high discriminative ability with minimal optimism. Full article