Sonolucent Cranial Implants: A Window into the Future of Management of Neurosurgical Patients? A Systematic Review and Cost Analysis.

BACKGROUND: Computeed tomography (CT) is a cornerstone of the identification and management of acute changes in neurosurgery patients. In addition to the monetary expense of CT scans, further costs are incurred due to the time of patient transport and radiation exposure. Ultrasounds (USs)offer a safe, inexpensive, and bedside alternative to CT but obstacles remain due to decreased penetrance in the adult skull. Sonolucent Cranial Implants (SCIs) offer a window for USs to view intracranial architectures. METHODS: The authors performed a PRISMA guidelines-based systematic review of the literature. Information was extracted from included articles in regards to illness pathology, US imaging feasibility, comparison to standard imaging, infections, and revisions. Costs were collected in regards to price of implant and follow-up imaging. RESULTS: A total of 226 articles resulted, of which 5 were included in the study. Ninety non-duplicate patients who received SCIs were analyzed. The pathologies of included patients is as follows: 51 patients were after extracranial-intracranial bypass, 37 after ventriculoperitoneal shunt placement for hydrocephalus, 1 after tumor resection, and 1 after cranioplasty following decompressive hemicraniectomy. All studies noted feasibility of US and comparability to standard imaging following SCI placement. Follow-up imaging with trans-sonolucent cranial implant ultrasound was estimated to save up to $4,000 per patient depending on the procedure. CONCLUSIONS: Initial studies suggest that US imaging through SCIs is a safe and efficacious alternative to CT imaging in neurosurgical patients. Cost analysis suggests that SCI and subsequent US can offer a cost savings compared with current treatment.

Endoscopic Endonasal Transsphenoidal Surgery for the Resection of Pituitary Adenomas: A Prime Candidate for a Shortened Length of Stay Enhanced Recovery after Surgery Protocol? A Systematic Review.

BACKGROUND: Enhanced Recovery After Surgery (ERAS) is a perioperative model of care aimed at optimizing postoperative rehabilitation and reducing hospital length of stay (LOS). Decreasing LOS avoids hospital-acquired complications, reduces cost of care, and improves patient satisfaction. Given the lack of ERAS protocols for endoscopic endonasal transsphenoidal surgery (EETS) resection of pituitary adenomas, a systematic review of EETS was performed to compile patient outcomes and analyze factors that may lead to increased LOS, reoperation, and readmission rates with the intention to contribute to the development of a successful ERAS protocol for EETS. METHODS: The authors performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines-based systematic review of the literature. Information was extracted regarding patient LOS, surgery complications, and readmission/reoperation rates. Pearson's correlations to LOS and reoperation/readmission rates were performed with variables normalized to the number of participants. Statistical significance was set at P value <0.05. RESULTS: Fourteen studies were included, consisting of 2083 patients. The most common complications were cerebrospinal fluid leaks (37%) and postoperative diabetes insipidus (DI) (9%). Transient DI was significantly correlated with shorter LOS. Functional pituitary adenomas were significantly correlated with lower readmission rates while nonfunctional pituitary adenomas were correlated with higher readmission rates. No other factor was found to be significantly correlated with a change in LOS or reoperation rate. CONCLUSIONS: EETS may be an ideal candidate for the development of ERAS cranial protocols. While our data largely supports the safe implementation of shortened LOS protocols in EETS, our findings highlight the importance of transient DI and nonfunctional pituitary adenomas management when formulating ERAS protocols.

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.

Excelencia en Neurocirugía: María Cristina García-Sancho, the First Latina Neurosurgeon in the World.

BACKGROUND: Neurosurgery is a specialty that has been dominated by males. Although there has been an increase in the number of women in the field, it is not yet close to being equal. Some noteworthy women who have carved the path for other women to follow in their footsteps include Drs. Sofía Ionescu and Diana Beck, the first and second female neurosurgeons worldwide, respectively. However, there are limited publications on Dr. María Cristina García-Sancho, the first Latina neurosurgeon. METHODS: The purpose of this review was to illuminate the neurosurgical community on the life of Dr. García-Sancho. A thorough literature was performed on medical and non-medical publications that were either authored by Dr. García-Sancho or mentioned her directly. RESULTS: Dr. García-Sancho earned her medical degree at the School of Medicine of the National Autonomous University of Mexico under the guidance of Dr. Clemente Robles, who founded Mexico's first neurosurgical department. Her training took her worldwide. CONCLUSIONS: Her expertise allowed her to pioneer a revolutionary advancement known as the one-step bilateral cordotomy. Her perseverance led her to becoming the head of the Department of Neurosurgery at the National Cancer Institute of Mexico and co-found the Mexican Society of Neurological Surgery, where she served on the board of directors. This review aims to advocate for an equitable environment in the field of neurosurgery with Dr. García-Sancho's story.

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.

Evaluating ChatGPT's Capabilities on Orthopedic Training Examinations: An Analysis of New Image Processing Features.

Introduction The efficacy of integrating artificial intelligence (AI) models like ChatGPT into the medical field, specifically orthopedic surgery, has yet to be fully determined. The most recent adaptation of ChatGPT that has yet to be explored is its image analysis capabilities. This study assesses ChatGPT's performance in answering Orthopedic In-Training Examination (OITE) questions, including those that require image analysis. Methods Questions from the 2014, 2015, 2021, and 2022 AAOS OITE were screened for inclusion. All questions without images were entered into ChatGPT 3.5 and 4.0 twice. Questions that necessitated the use of images were only entered into ChatGPT 4.0 twice, as this is the only version of the system that can analyze images. The responses were recorded and compared to AAOS's correct answers, evaluating the AI's accuracy and precision. Results A total of 940 questions were included in the final analysis (457 questions with images and 483 questions without images). ChatGPT 4.0 performed significantly better on questions that did not require image analysis (67.81% vs 47.59%, p<0.001). Discussion While the use of AI in orthopedics is an intriguing possibility, this evaluation demonstrates how, even with the addition of image processing capabilities, ChatGPT still falls short in terms of its accuracy. As AI technology evolves, ongoing research is vital to harness AI's potential effectively, ensuring it complements rather than attempts to replace the nuanced skills of orthopedic surgeons.

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 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.

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.

Realistic Assessment of Research Publications by Neurosurgery Residency Applicants.

OBJECTIVE: Neurosurgical residency applicants' prior research experience can amplify their ability to stand out to prospective neurosurgery programs. We attempted to accurately quantify the number of research publications coauthored by applicants by analyzing the publications of applicants who matched into neurosurgery in the 2021 Match. METHODS: Scopus, a peer-reviewed literature database, was queried for publications by applicants who matched into neurosurgery in the 2021 Match before the finalization of rank lists. Conference papers, abstracts, and book chapters were excluded to determine an accurate average of actual publications. Descriptive statistics for resident publication data were used, with a Mann-Whitney U test used to compare research productivity between male and female residents. RESULTS: There were 234 positions filled by the 2021 Match, and 233 neurosurgical residents were identifiable in this study. A total of 187 residents matching from U.S. Doctor of Medicine and Doctor of Osteopathic Medicine programs were identified with 946 total publications-an average of 5.1 publications per resident. Analysis of descriptive statistics revealed type of research conducted, authorship information, most published journals, and citation data. Significant differences were found in the number of publications between male and female applicants with averages of 5.6 and 3.8 publications, respectively. CONCLUSIONS: Students matriculating to neurosurgery residency programs display a wide range of research productivity. Typical U.S. Doctor of Medicine and Doctor of Osteopathic Medicine applicants have coauthored a mean of 5.1 and a median of 4.0 publications. This information may assist program directors in weighing applicants' research background and give medical students interested in the field reasonable research expectations.