RESUMO
BACKGROUND: Consumer product-related genital injuries in females across all age groups are understudied. Existing research focuses primarily on paediatric populations. We aimed to determine characteristics, trends and predictors of hospitalisation. METHODS: The National Electronic Injury Surveillance System database was queried for female genital injuries from 2013 to 2022. We stratified our population into four age groups (<18, 18-34, 35-54, >54 years). Automated text matching and manual reviews were employed for variable extraction. χ2 tests and logistic regression were conducted, accounting for survey design and weights. RESULTS: 9054 cases representing a national estimate of 252 329 injuries (95% CI 188 059 to 316 599) were identified. Paediatric injuries were most common (61%) and seniors had the highest hospitalisation rates (28%). Falls were common in paediatric (51%) and senior (48%) groups, whereas self-induced and topical application injuries were more frequent among adults aged 18-34 and 35-54. Injuries predominantly involved playground equipment and bicycles in children, razors and massage devices in adults aged 18-34 and 35-54 and household structures in seniors. Hospitalisation increased over the decade from 7% to 9%; significant predictors of hospitalisation were Asian race (OR=3.39, 95% CI 1.83 to 6.30), fractures (OR=7.98, 95% CI 4.85 to 13.1) and urethral injury (OR=3.15, 95% CI 1.30 to 7.63). CONCLUSIONS: Our study identifies distinct patterns in female genital injuries across ages. In the paediatric cohort, injuries are often linked to playgrounds and bicycles. For adults, grooming products are frequently implicated. Seniors commonly suffer injuries from household structures such as bathtubs. These patterns may inform discussions on tailored preventive strategies.
RESUMO
BACKGROUND: Large language models including GPT-4 (OpenAI) have opened new avenues in health care and qualitative research. Traditional qualitative methods are time-consuming and require expertise to capture nuance. Although large language models have demonstrated enhanced contextual understanding and inferencing compared with traditional natural language processing, their performance in qualitative analysis versus that of humans remains unexplored. OBJECTIVE: We evaluated the effectiveness of GPT-4 versus human researchers in qualitative analysis of interviews with patients with adult-acquired buried penis (AABP). METHODS: Qualitative data were obtained from semistructured interviews with 20 patients with AABP. Human analysis involved a structured 3-stage process-initial observations, line-by-line coding, and consensus discussions to refine themes. In contrast, artificial intelligence (AI) analysis with GPT-4 underwent two phases: (1) a naïve phase, where GPT-4 outputs were independently evaluated by a blinded reviewer to identify themes and subthemes and (2) a comparison phase, where AI-generated themes were compared with human-identified themes to assess agreement. We used a general qualitative description approach. RESULTS: The study population (N=20) comprised predominantly White (17/20, 85%), married (12/20, 60%), heterosexual (19/20, 95%) men, with a mean age of 58.8 years and BMI of 41.1 kg/m2. Human qualitative analysis identified "urinary issues" in 95% (19/20) and GPT-4 in 75% (15/20) of interviews, with the subtheme "spray or stream" noted in 60% (12/20) and 35% (7/20), respectively. "Sexual issues" were prominent (19/20, 95% humans vs 16/20, 80% GPT-4), although humans identified a wider range of subthemes, including "pain with sex or masturbation" (7/20, 35%) and "difficulty with sex or masturbation" (4/20, 20%). Both analyses similarly highlighted "mental health issues" (11/20, 55%, both), although humans coded "depression" more frequently (10/20, 50% humans vs 4/20, 20% GPT-4). Humans frequently cited "issues using public restrooms" (12/20, 60%) as impacting social life, whereas GPT-4 emphasized "struggles with romantic relationships" (9/20, 45%). "Hygiene issues" were consistently recognized (14/20, 70% humans vs 13/20, 65% GPT-4). Humans uniquely identified "contributing factors" as a theme in all interviews. There was moderate agreement between human and GPT-4 coding (κ=0.401). Reliability assessments of GPT-4's analyses showed consistent coding for themes including "body image struggles," "chronic pain" (10/10, 100%), and "depression" (9/10, 90%). Other themes like "motivation for surgery" and "weight challenges" were reliably coded (8/10, 80%), while less frequent themes were variably identified across multiple iterations. CONCLUSIONS: Large language models including GPT-4 can effectively identify key themes in analyzing qualitative health care data, showing moderate agreement with human analysis. While human analysis provided a richer diversity of subthemes, the consistency of AI suggests its use as a complementary tool in qualitative research. With AI rapidly advancing, future studies should iterate analyses and circumvent token limitations by segmenting data, furthering the breadth and depth of large language model-driven qualitative analyses.