Analysis of Concussions Reported in American Professional Football Injury Reports in the 2019 Through 2023 Regular Seasons.

OBJECTIVE: The National Football League (NFL) has seen increasing scrutiny regarding its management of concussions, especially following an on-field incident involving the Miami Dolphins' quarterback Tua Tagovailoa in the 2022 season. We hope to elucidate the recent trends in the diagnosis and management of concussions during the course of 5 NFL seasons from 2019 to 2023. METHODS: We queried the NFL injury reports from the 2019 through 2023 database recording players listed with concussions. The weeks missed were calculated using the NFL game logs. Players' concussions that did not occur in the games, those complicated by other injuries, and those affected by roster status were excluded. RESULTS: Searches of the NFL injury reports resulted in the identification of 664 of 692 concussions (96%) that occurred in regular season games across the 2019-2023 seasons. During the course of these 5 seasons, 31% of the players returned without missing a game, 39% of the players missed 1 game, and 30% of the players missed ≥2 games. No significant difference in the number of concussions per game or weeks missed was observed across the seasons observed. Players with concussions on teams that made the playoffs saw fewer weeks missed than those on non-playoff teams (0.86 vs. 1.37; P = 0.002). CONCLUSIONS: Since the start of the 2021 NFL season, an increasing incidence of concussions has been noted; however, there was no change observed in the number of weeks missed after the concussions. Trends in the rates of concussions across the seasons remain largely stable, despite increased scrutiny over concussions in the sport.

Can Artificial Intelligence Mitigate Missed Diagnoses by Generating Differential Diagnoses for Neurosurgeons?

BACKGROUND/OBJECTIVE: Neurosurgery emphasizes the criticality of accurate differential diagnoses, with diagnostic delays posing significant health and economic challenges. As large language models (LLMs) emerge as transformative tools in healthcare, this study seeks to elucidate their role in assisting neurosurgeons with the differential diagnosis process, especially during preliminary consultations. METHODS: This study employed 3 chat-based LLMs, ChatGPT (versions 3.5 and 4.0), Perplexity AI, and Bard AI, to evaluate their diagnostic accuracy. Each LLM was prompted using clinical vignettes, and their responses were recorded to generate differential diagnoses for 20 common and uncommon neurosurgical disorders. Disease-specific prompts were crafted using Dynamed, a clinical reference tool. The accuracy of the LLMs was determined based on their ability to identify the target disease within their top differential diagnoses correctly. RESULTS: For the initial differential, ChatGPT 3.5 achieved an accuracy of 52.63%, while ChatGPT 4.0 performed slightly better at 53.68%. Perplexity AI and Bard AI demonstrated 40.00% and 29.47% accuracy, respectively. As the number of considered differentials increased from 2 to 5, ChatGPT 3.5 reached its peak accuracy of 77.89% for the top 5 differentials. Bard AI and Perplexity AI had varied performances, with Bard AI improving in the top 5 differentials at 62.11%. On a disease-specific note, the LLMs excelled in diagnosing conditions like epilepsy and cervical spine stenosis but faced challenges with more complex diseases such as Moyamoya disease and amyotrophic lateral sclerosis. CONCLUSIONS: LLMs showcase the potential to enhance diagnostic accuracy and decrease the incidence of missed diagnoses in neurosurgery.

Impact of environmental pollutants on pediatric brain tumor incidence in New Jersey.

OBJECTIVE: The relationship between environmental contaminants and brain tumor incidence in adults has been thoroughly explored but research into how these contaminants affect pediatric brain tumor (PBT) incidence has not been explored. Children, typically having more limited geographical movement and thus more consistent environmental contaminant exposure, might offer more reliable insights into which environmental contaminants affect the incidence of brain tumors. The present study is the first to focus on exploring whether a possible association exists between the incidence of PBTs and exposure to environmental pollutants in New Jersey (NJ). METHODS: Linear regressions were run between PBT incidence and the concentration of air quality pollutants such as Ozone (O3), Particulate Matter 2.5 (PM2.5), Particulate Matter 10 (PM10), and Carbon Monoxide (CO). Similarly, linear regressions were run between PBT incidence and Elevated Blood Lead Levels (BLL). RESULTS: The study observed a significant positive relationship between O3 and PBT incidence (ß = 0.34, p = 0.028). However, the relationship between PBT incidence, and environmental pollutants such as CO (ß = 0.0047, p = 0.098), PM2.5 (ß = -0.2624, p = 0.74), and PM10 (ß = -0.7353, p = 0.073) were found to be nonsignificant. For elevated BLL, nonsignificant relationships with PBT incidence were observed at 10-14 µg/dL (ß = -39.38, p = 0.30), 15-19 µg/dL (ß = -67.00, p = 0.21), and 20-44 µg/dL (ß = -201.98, p = 0.12). CONCLUSIONS: The results indicate a possible impact of O3 on the incidence of PBTs in NJ. In contrast to the significant links found in prior studies of adult brain tumors, the associations between PBT occurrence and particulate matter were not significant. These findings highlight the importance of further investigating how environmental factors, especially O3, relate to PBTs.

Assessing Diversity, Equity, and Inclusion in Patient-Facing Websites in Neurosurgical Departments in the United States.

BACKGROUND: Patient-facing websites serve as essential platforms for disseminating information, engaging with patients, and increasing access to neurosurgical resources and services. Diversity, Equity, and Inclusion are at the forefront of issues facing the field of neurosurgery, especially concerning race and gender disparities in regards to providers in the field. METHODS: Data were collected in regards to the race and gender of patients and providers displayed on the neurosurgery department's patient-facing website in addition to accommodations for disabilities, decreased ability to pay, and language. RESULTS: Patients who were White were depicted more commonly than those of color (69% vs. 31%, P < 0.00001). White patients also were over-represented when compared with the average demographics of the communities in which the hospitals served (P = 0.03846). Neurosurgical providers who were White outnumbered those of color (70% vs. 30%, P < 0.00001). The racial depiction of providers was comparable with racial disparities currently observed in neurosurgery (P = 0.59612). Female neurosurgery providers were seen less than male providers on patient-facing websites (P < 0.00001) but were seen more commonly on patient-facing websites than the percentage of practicing neurosurgeons they currently comprise (28% vs. 8%, P < 0.00001). CONCLUSIONS: The results of this study suggest that patient-facing websites of neurosurgical departments are an area of improvement in regards to Diversity, Equity, and Inclusion in the field of neurosurgery. Disparities are noted in regards to the racial depiction of patients and further call to attention racial and gender disparities in the field of neurosurgery.

Can Publicly Available Artificial Intelligence Successfully Identify Current Procedural Terminology Codes for Common Procedures in Neurosurgery?

OBJECTIVE: Coding for neurosurgical procedures is a complex process that is dynamically changing year to year, through the annual introduction and removal of codes and modifiers. The authors hoped to elucidate if publicly available artificial intelligence (AI) could offer solutions for neurosurgeons with regard to coding. METHODS: Multiple publicly available AI platforms were asked to provide Current Procedural Terminology (CPT) codes and Revenue Value Units (RVU) values for common neurosurgical procedures of the brain and spine with a given indication for the procedure. The responses of platforms were recorded and compared to the currently valid CPT codes used for the procedure and the amount of RVUs that would be gained. RESULTS: Six platforms and Google were asked for the appropriate CPT codes for 10 endovascular, spinal, and cranial procedures each. The highest performing platforms were as follows: Perplexity.AI identified 70% of endovascular, BingAI identified 55% of spinal, and ChatGPT 4.0 with Bing identified 75% of cranial CPT codes. With regard to RVUs, the top performer gained 78% of endovascular, 42% of spinal, and 70% of cranial possible RVUs. With regard to accuracy, AI platforms on average outperformed Google (45% vs. 25%, P = 0.04236). CONCLUSIONS: The ability of publicly available AIs to successfully code for neurosurgical procedures holds great promise in the future. Future development of AI should focus on improving accuracy with regard to CPT codes and providing supporting documentation for its decisions. Improvement on the existing capabilities of AI platforms can allow for increased operational efficiency and cost savings for practices.