Social determinants of health and outcome disparities in spine tumor surgery. Part 2: Neighborhood disadvantage and long-term outcomes.

OBJECTIVE: Neighborhood-level resource disadvantage has been previously shown to predict extent of resection, oncological follow-up, adjuvant treatment, and clinical trial participation for malignancies, including glioblastoma. The authors aimed to characterize the association between neighborhood disadvantage and long-term outcomes after spine tumor surgery. METHODS: The authors analyzed all patients who underwent surgery for primary or secondary (all metastatic pathologies) spine tumors at a single spinal oncology specialty center in the United States from 2015 to 2022. The Area Deprivation Index (ADI), a validated metric compositing 17 social determinants of health variables that ranges continuously from 0% (higher advantage) to 100% (higher disadvantage), was used to quantify neighborhood disadvantage. Patient addresses were matched to ADI on the basis of the census block of residence. Subsequently, the study population was dichotomized into advantaged (ADI 0%-33%) and disadvantaged (ADI 34%-100%) cohorts. The primary endpoint was functional status, as defined by Eastern Cooperative Oncology Group (ECOG) Performance Status Scale grade, with secondary endpoints including inpatient outcomes, mortality, readmissions, reoperations, and clinical research participation. Multivariable logistic, gamma log-link, and Cox regression adjusted for 14 confounders, including patient and oncological characteristics, general and tumor-related presenting severity, and treatment. RESULTS: In total, 237 patients underwent spine tumor surgery from 2015 to 2022, with an average age of 53.9 years, and 57.0% had primary tumors whereas 43.0% had secondary tumors; 55.3% (n = 131) were classified by ADI into the disadvantaged cohort. This cohort had higher rates of ambulation deficits on presentation (39.1% vs 23.5%, p = 0.015) and nonelective surgery (35.1% vs 23.6%, p = 0.030). Postoperatively, disadvantaged patients exhibited higher odds of residual tumor (OR 2.55, p = 0.026), especially for secondary tumors (OR 4.92, p = 0.045). Patients from disadvantaged neighborhoods additionally exhibited significantly higher odds of poor functional status at follow-up (OR 3.94, p = 0.002). Postoperative survival was 74.7% (mean follow-up 17.6 months), with the disadvantaged cohort experiencing significantly shorter survival (HR 1.92, p = 0.049). Moreover, this population had higher odds of readmission (OR 1.92, p = 0.046) and, for primary tumors, reoperation (OR 9.26, p = 0.005). Elective participation in prospective clinical research was lower among the disadvantaged cohort (OR 0.45, p = 0.016). CONCLUSIONS: Neighborhood disadvantage predicts higher rates of residual tumor, readmission, and reoperation, as well as poorer functional status, shorter postoperative survival, and decreased elective research participation. The ADI may be used to risk stratify spine oncology patients and guide targeted interventions to ameliorate neurosurgical disparities and to reduce barriers to research participation.

Social determinants of health and outcome disparities in spine tumor surgery. Part 1: An analysis of 6.6 million nationwide admissions.

OBJECTIVE: Earlier research has demonstrated that social determinants of health (SDoH) impact neurosurgical access and outcomes, but these trends are less characterized for spine tumors relative to intracranial tumors. The authors aimed to elucidate the association between SDoH and outcomes for a nationwide cohort of spine tumor surgery admissions. METHODS: The authors identified all admissions with a spine tumor diagnosis in the National Inpatient Sample (NIS) from 2002 to 2019. Four SDoH were analyzed: race and ethnicity, insurance, household income, and safety-net hospital (SNH) treatment. Hospitals in the top quartile of safety-net burden (in terms of percentage of patients receiving Medicaid or uninsured) were categorized as SNHs. Multivariable regression queried the association between 22 variables and 5 perioperative outcomes: mortality, discharge disposition, complications, length of stay (LOS), and hospitalization costs. Interaction term analysis with hospitalization year was used to assess longitudinal changes in outcome disparities. Finally, the authors constructed random forest machine learning models to assess the impact of SDoH variables on prognostic accuracy and to quantify the relative importance of predictors for disposition. RESULTS: Of 6,593,392 total admissions with spine tumors, 219,380 (3.3%) underwent surgery. Non-White race (OR 0.80-0.91, p < 0.001) and nonprivate insurance (OR 0.76-0.83, p < 0.001) were associated with lower odds of receiving surgery. Among surgical admissions, presenting severity, including of myelopathy and plegia, was elevated among non-White, nonprivate insurance, and low-income admissions (all p < 0.001). Black race (OR 0.70, p < 0.001), Medicare (OR 0.70, p < 0.001), Medicaid (OR 0.90, p < 0.001), and lower income (OR 0.88-0.93, all p < 0.001) were associated with decreased odds of favorable discharge disposition. Increased LOS and costs were observed among non-White (+6%-10% in LOS and +5%-9% in costs, both p < 0.001) and Medicaid (+16% in LOS and +6% in costs, both p < 0.001) admissions. SNH treatment was also associated with higher mortality (OR 1.49, p < 0.001) and complication (OR 1.20, p < 0.001) rates. From 2002 to 2019, disposition improved annually for Medicaid patients (OR 1.03 per year, p = 0.022) but worsened for Black patients (OR 0.98 per year, p = 0.046). Random forest models identified household income as the most important predictor of discharge disposition. CONCLUSIONS: For spine tumor admissions, SDoH predicted surgical intervention, presenting severity, and perioperative outcomes. Over 2 decades, disparities improved for Medicaid patients but worsened for Black patients. Finally, SDoH significantly improve prognostic accuracy for outcomes after spine tumor surgery. Further study toward ameliorating patient disparities for this population is warranted.

Evidence Against a Traumatic Brain Injury "July Effect": An Analysis of 3 160 452 Patients From the National Inpatient Sample.

BACKGROUND AND OBJECTIVES: The "July Effect" hypothesizes increased morbidity and mortality after the addition of inexperienced physicians at the beginning of an academic year. However, the impact of newer members on neurosurgical teams managing patients with traumatic brain injury (TBI) has yet to be examined. This study conducted a nationwide analysis to evaluate the existence of the "July Effect" in the setting of patients with TBI. METHODS: The Healthcare Cost and Utilization Project Central Distributor's National Inpatient Sample data set was queried for patients with TBI using International Classification of Diseases (ICD)-9 and ICD-10 codes. Discharges were included for diagnoses of traumatic epidural, subdural, or subarachnoid hemorrhages. Only patients treated at teaching hospitals were included to ensure resident involvement in care. Patients were grouped into July admission and non-July admission cohorts. A subgroup of patients with neurotrauma undergoing any form of cranial surgery was created. Perioperative variables were recorded. Rates of different complications were assayed. Groups were compared using χ2 tests (qualitative variables) and t-tests or Mann-Whitney U-tests (quantitative variables). Logistic regression was used for binary variables. Gamma log-linked regression was used for continuous variables. RESULTS: The National Inpatient Sample database yielded a weighted average of 3 160 452 patients, of which 312 863 (9.9%) underwent surgical management. Patients admitted to the hospital in July had a 5% decreased likelihood of death (P = .027), and a 5.83% decreased likelihood of developing a complication (P < .001) compared with other months of the year. July admittance to a hospital showed no significant impact on mean length of stay (P = .392) or routine discharge (P = .147). Among patients with TBI who received surgical intervention, July admittance did not significantly affect the likelihood of death (P = .053), developing a complication (P = .477), routine discharge (P = .986), or mean length of stay (P = .385). CONCLUSION: The findings suggested that there is no "July Effect" on patients with TBI treated at teaching hospitals in the United States.

Comparison of Surgical Modalities for Giant Pituitary Adenoma: A Systematic Review and Meta-Analysis of 1413 Patients.

BACKGROUND AND OBJECTIVES: Giant pituitary adenomas (GPAs) are a challenging clinical entity, composing 5% to 15% of all pituitary adenomas. While the endoscopic endonasal transsphenoidal (EET) approach has surpassed the microsurgical transsphenoidal (MT) and transcranial (TC) approaches as the first-line surgical modality in most institutions, a systematic review comparing the 3 approaches has not been undertaken since 2012. Given growing adoption of EET and development of novel operative techniques over the past decade, an updated comparison of GPA surgical modalities is warranted. METHODS: We identified all studies related to the surgical management of GPAs in PubMed, Embase, and Web of Science from inception to December 31, 2021. End points assessed included gross total resection (GTR) rates, postoperative visual improvement, mortality, and perioperative complications. RESULTS: After screening of 1701 studies, we identified 45 studies on the surgical management of GPAs for meta-analysis. Thirty-one used the EET approach (n = 1413), 11 studies used the MT approach (n = 601), and 10 used the TC approach (n = 416). The cumulative number of patients treated by EET did not exceed that of patients treated by the TC or MT approaches until 2014 and 2015, respectively. Despite patients undergoing EET having the highest average tumor diameter, pooled rates for GTR were significantly higher for EET (42%) than MT (33%, P < .001) and TC (8%, P < .001) and EET similarly exhibited superior rates of visual improvement (85%) than MT (73%, P < .001) and TC (56%, P < .001). Mortality rates were comparable between EET (0.6%) and MT (1.6%), but EET had significantly lower mortality than TC (2.7%, P < .001). Compared with MT, EET had lower rates of hypopituitarism (8.5% vs 14.9%, P = .012) but higher rates of diabetes insipidus (3.1% vs 0.5%, P = .001). CONCLUSION: In an updated meta-analysis of 1413 patients with GPA, EET resection conferred significantly higher rates of visual improvement and GTR, when compared with the MT and TC approaches.

Factors influencing disparities in epilepsy surgery: analysis of the National Inpatient Sample and Kids' Inpatient Database.

OBJECTIVE: Despite the proven efficacy of surgical intervention for achieving seizure freedom and improved quality of life for many epilepsy patients, this treatment remains underutilized. In this study, the authors assessed sociodemographic trends in epilepsy surgery in the National Inpatient Sample (NIS) and the Kids' Inpatient Database (KID) and sought to determine whether disparities in surgical intervention for epilepsy may be attributed to insurance and comorbidity status. METHODS: This cross-sectional study utilized data from the NIS database and KID from the Healthcare Cost and Utilization Project between the years 2012 and 2018. Outcomes of interest were rates of neurosurgical intervention, including resection, neuromodulation, or laser ablation. The authors utilized logit regression models to test the association between rates of neurosurgical intervention and the variables of interest and calculated the adjusted mean proportion of patients who received surgery using marginal effects. RESULTS: Of 336,015 admissions with intractable epilepsy in the NIS, 6.1% were patients who underwent neurosurgical treatment. Of 39,655 admissions from KID, 5.0% received surgical treatment. Private insurance was associated with a greater odds of surgical intervention compared with Medicaid (NIS: OR 1.63, KID: OR 1.62; p < 0.001). Patients assigned White race had an increased odds ratio of undergoing surgery when compared with those assigned Black race, adjusted for comorbidity burden (NIS: OR 1.59, p < 0.001; KID: OR 1.44, p = 0.027). Patients with an Elixhauser Comorbidity Index score of 0 or 1 were associated with a lower likelihood of surgery when compared to their higher scoring counterparts who had 4 or more comorbidities (NIS: OR 0.74, KID: OR 0.62; both p < 0.001). CONCLUSIONS: This study demonstrates that marginalized patients and those with Medicaid had decreased odds of neurosurgical intervention for epilepsy. Results of this research support the need for increased attention toward epilepsy patients from marginalized groups. Further investigation into the root cause of socioeconomic inequities in epilepsy surgery is necessary.

Endoluminal Sigmoid Sinus Occlusion During Jugular Foramen Tumor Surgery: Novel Technique, Operative Nuances, and Clinical Experience With 33 Patients.

BACKGROUND AND IMPORTANCE: Surgery of jugular foramen tumors (JFTs) often requires vascular control by means of ligating the internal jugular vein and sigmoid sinus (SS) to allow intrabulbar access. Occlusion of the SS traditionally involves presigmoid and retrosigmoid durotomies allowing introduction of ligature devices, predisposing to cerebrospinal fluid (CSF) leakage and pseudomeningoceles. We describe a simple and novel endoluminal sigmoid sinus occlusion (ESSO) technique with Gelfoam that is entirely extradural. CLINICAL PRESENTATION: An extended anterolateral infralabyrinthine approach with ESSO was performed in 33 patients with JFTs. After ligating the internal jugular vein, the SS is opened and Gelfoam is placed endoluminally into the proximal SS. Care is taken to avoid occlusion of the venous outflow of the vein of Labbe to avoid temporal lobe venous infarction. Hemostatic gelatin matrix is injected distally to stop venous backflow from the inferior petrosal sinus. The jugular venous system is isolated, and the outer jugular wall can be opened to expose the JFT for resection. There were no complications of temporal lobe venous infarction or postoperative hematoma observed. Four patients with intradural tumor extension developed pseudomeningoceles. For patients with purely extradural JFTs, none developed postoperative incisional CSF leaks and one had pseudomeningocele. CONCLUSION: This ESSO technique is fast and effective, permitting occlusion of the SS during JFT surgery. It has the advantage of being entirely extradural, avoiding durotomy which can result in postoperative CSF leak. It is important to keep the Gelfoam distal to the transverse-sigmoid junction to avoid occlusion of the vein of Labbe inlet and temporal lobe venous infarction.

Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach.

Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients' comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4's potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers' consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American's reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.

Derivation and Internal Validation of a Mortality Prognostication Machine Learning Model in Ebola Virus Disease Based on Iterative Point-of-Care Biomarkers.

Background: Although multiple prognostic models exist for Ebola virus disease mortality, few incorporate biomarkers, and none has used longitudinal point-of-care serum testing throughout Ebola treatment center care. Methods: This retrospective study evaluated adult patients with Ebola virus disease during the 10th outbreak in the Democratic Republic of Congo. Ebola virus cycle threshold (Ct; based on reverse transcriptase polymerase chain reaction) and point-of-care serum biomarker values were collected throughout Ebola treatment center care. Four iterative machine learning models were created for prognosis of mortality. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days 1 and 2, days 3 and 4, and days 5 and 6 associated with mortality were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided period-specific areas under curve with 95% CIs. Results: Of 310 cases positive for Ebola virus disease, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium; low albumin during days 1 and 2; elevated C-reactive protein, BUN, and potassium during days 3 and 4; and elevated C-reactive protein and BUN during days 5 and 6. The area under curve substantially improved with each iteration: base model, 0.74 (95% CI, .69-.80); days 1 and 2, 0.84 (95% CI, .73-.94); days 3 and 4, 0.94 (95% CI, .88-1.0); and days 5 and 6, 0.96 (95% CI, .90-1.0). Conclusions: This is the first study to utilize iterative point-of-care biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to 6 days into patient care.

Repeated peripheral infusions of anti-EGFRvIII CAR T cells in combination with pembrolizumab show no efficacy in glioblastoma: a phase 1 trial.

We previously showed that chimeric antigen receptor (CAR) T-cell therapy targeting epidermal growth factor receptor variant III (EGFRvIII) produces upregulation of programmed death-ligand 1 (PD-L1) in the tumor microenvironment (TME). Here we conducted a phase 1 trial (NCT03726515) of CAR T-EGFRvIII cells administered concomitantly with the anti-PD1 (aPD1) monoclonal antibody pembrolizumab in patients with newly diagnosed, EGFRvIII+ glioblastoma (GBM) (n = 7). The primary outcome was safety, and no dose-limiting toxicity was observed. Secondary outcomes included median progression-free survival (5.2 months; 90% confidence interval (CI), 2.9-6.0 months) and median overall survival (11.8 months; 90% CI, 9.2-14.2 months). In exploratory analyses, comparison of the TME in tumors harvested before versus after CAR + aPD1 administration demonstrated substantial evolution of the infiltrating myeloid and T cells, with more exhausted, regulatory, and interferon (IFN)-stimulated T cells at relapse. Our study suggests that the combination of CAR T cells and PD-1 inhibition in GBM is safe and biologically active but, given the lack of efficacy, also indicates a need to consider alternative strategies.

Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators.

Importance: The progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes. Objective: To gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees. Design, Setting, and Participants: The study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2023. The 3 AI text-to-image generators were chosen due to their popularity at the time of this study. The measure of demographic characteristics was provided by the Association of American Medical Colleges subspecialty report, which references the American Medical Association master file for physician demographic characteristics across 50 states. Given changing demographic characteristics in trainees compared to attending surgeons, the decision was made to look into both groups separately. Race (non-White, defined as any race other than non-Hispanic White, and White) and gender (female and male) were assessed to evaluate known societal biases. Exposures: Images were generated using a prompt template, "a photo of the face of a [blank]", with the blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries on 3 continents (the US, Nigeria, and China). Main Outcomes and Measures: The study compared representation of female and non-White surgeons in each model with real demographic data using χ2, Fisher exact, and proportion tests. Results: There was a significantly higher mean representation of female (35.8% vs 14.7%; P < .001) and non-White (37.4% vs 22.8%; P < .001) surgeons among trainees than attending surgeons. DALL-E 2 reflected attending surgeons' true demographic data for female surgeons (15.9% vs 14.7%; P = .39) and non-White surgeons (22.6% vs 22.8%; P = .92) but underestimated trainees' representation for both female (15.9% vs 35.8%; P < .001) and non-White (22.6% vs 37.4%; P < .001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively; P < .001) and non-White (0.5% and 0.6%, respectively; P < .001) surgeons than DALL-E 2 or true demographic data. Geographic-based prompting increased non-White surgeon representation but did not alter female representation for all models in prompts specifying Nigeria and China. Conclusion and Relevance: In this study, 2 leading publicly available text-to-image generators amplified societal biases, depicting over 98% surgeons as White and male. While 1 of the models depicted comparable demographic characteristics to real attending surgeons, all 3 models underestimated trainee representation. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.

Diagnostic Accuracy of the World Health Organization Pediatric Emergency Triage, Assessment and Treatment Tool Plus Among Patients Seeking Care in Nairobi, Kenya: A Cross-sectional Study.

INTRODUCTION: The World Health Organization developed Emergency Triage Assessment and Treatment Plus (ETAT+) guidelines to facilitate pediatric care in resource-limited settings. ETAT+ triages patients as nonurgent, priority, or emergency cases, but there is limited research on the performance of ETAT+ regarding patient-oriented outcomes. This study assessed the diagnostic accuracy of ETAT+ in predicting the need for hospital admission in a pediatric emergency unit at Kenyatta National Hospital in Nairobi, Kenya. METHODS: This was a secondary analysis of a cross-sectional study of pediatric emergency unit patients enrolled over a 4-week period using fixed random sampling. Diagnostic accuracy of ETAT+ was evaluated using receiver operating curves (ROCs) and respective 95% confidence intervals (CIs) with associated sensitivity and specificity (reference category: nonurgent). The ROC analysis was performed for the overall population and stratified by age group. RESULTS: A total of 323 patients were studied. The most common reasons for presentation were upper respiratory tract disease (32.8%), gastrointestinal disease (15.5%), and lower respiratory tract disease (12.4%). Two hundred twelve participants were triaged as nonurgent (65.6%), 60 as priority (18.6%), and 51 as emergency (15.8%). In the overall study population, the area under the ROC curve was 0.97 (95% CI, 0.95-0.99). The ETAT+ sensitivity was 93.8% (95% CI, 87.0%-99.0%), and the specificity was 82.0% (95% CI, 77.0%-87.0%) for admission of priority group patients. The sensitivity and specificity for the emergency patients were 66.0% (95% CI, 55.0%-77.0%) and 98.0% (95% CI, 97.0%-100.0%), respectively. CONCLUSIONS: ETAT+ demonstrated diagnostic accuracy for predicting patient need for hospital admission. This finding supports the utility of ETAT+ to inform emergency care practice. Further research on ETAT+ performance in larger populations and additional patient-oriented outcomes would enhance its generalizability and application in resource-limited settings.

The epidemiology of crib-related head injuries: A ten-year nationwide analysis.

INTRODUCTION: Falls from cribs resulting in head injury are understudied and poorly characterized. The purpose of this study was to advance current understanding of the prevalence, descriptive characteristics of injury victims, and the types of crib fall-related head injuries (CFHI) using queried patient cases from the National Electronic Injury Surveillance System (NEISS) database. METHODS: Using the US Consumer Product Safety Commission's System NEISS database, we queried all CFHIs among children from over 100 emergency departments (EDs). Patient information regarding age, race, sex, location of the incident, diagnoses, ED disposition, and sequelae were analyzed. The number of CFHI from all US EDs during each year was also collected from the database. RESULTS: There were an estimated 54,799 (95% CI: 30,228-79,369) total visits to EDs for CFHIs between 2012 and 2021, with a decrease in incidence of approximately 20% during the onset of the COVID-19 pandemic (2019: 5616 cases, 2020: 4459 cases). The annual incidence of injuries showed no significant trend over the 10-year study period. An available subset of 1782 cases of head injuries from approximately 100 EDs was analyzed, and 1442 cases were included in final analysis. Injuries were sorted into three primary categories: unspecified closed head injury (e.g., closed head injury, blunt head trauma, or traumatic brain injury), concussion, or open head injury and skull fracture. Unspecified closed head injuries were the most common of all head injuries (95.4%, 1376/1442). Open head injuries (14/1442, 0.97%) and concussions 3.6% (52/1442, 3.6%) were rare. Most injuries involved children under the age of 1 (42.6%) compared to children who were 1, 2, 3, or 4-years old. About a fourth of patients had other diagnoses in addition to their primary injury including scalp/forehead hematomas, emesis, and contusions. Female patients were more likely to present with other diagnoses in addition to their primary head injury (Difference: 12.3%, 95% CI: 9.87%-15.4%, p < .0001). CONCLUSION: Despite minimum rail height requirements set by the Consumer Safety Product Commission (CPSC), head injuries associated with crib falls are prevalent in the United States. However, most injuries were minor with a vast majority of patients being released following examination and treatment.

Optimizing CAR-T Therapy for Glioblastoma.

Chimeric antigen receptor T-cell therapies have transformed the management of hematologic malignancies but have not yet demonstrated consistent efficacy in solid tumors. Glioblastoma is the most common primary malignant brain tumor in adults and remains a major unmet medical need. Attempts at harnessing the potential of chimeric antigen receptor T-cell therapy for glioblastoma have resulted in glimpses of promise but have been met with substantial challenges. In this focused review, we discuss current and future strategies being developed to optimize chimeric antigen receptor T cells for efficacy in patients with glioblastoma, including the identification and characterization of new target antigens, reversal of T-cell dysfunction with novel chimeric antigen receptor constructs, regulatable platforms, and gene knockout strategies, and the use of combination therapies to overcome the immune-hostile microenvironment.

Performance of ChatGPT and GPT-4 on Neurosurgery Written Board Examinations.

BACKGROUND AND OBJECTIVES: Interest surrounding generative large language models (LLMs) has rapidly grown. Although ChatGPT (GPT-3.5), a general LLM, has shown near-passing performance on medical student board examinations, the performance of ChatGPT or its successor GPT-4 on specialized examinations and the factors affecting accuracy remain unclear. This study aims to assess the performance of ChatGPT and GPT-4 on a 500-question mock neurosurgical written board examination. METHODS: The Self-Assessment Neurosurgery Examinations (SANS) American Board of Neurological Surgery Self-Assessment Examination 1 was used to evaluate ChatGPT and GPT-4. Questions were in single best answer, multiple-choice format. χ 2 , Fisher exact, and univariable logistic regression tests were used to assess performance differences in relation to question characteristics. RESULTS: ChatGPT (GPT-3.5) and GPT-4 achieved scores of 73.4% (95% CI: 69.3%-77.2%) and 83.4% (95% CI: 79.8%-86.5%), respectively, relative to the user average of 72.8% (95% CI: 68.6%-76.6%). Both LLMs exceeded last year's passing threshold of 69%. Although scores between ChatGPT and question bank users were equivalent ( P = .963), GPT-4 outperformed both (both P < .001). GPT-4 answered every question answered correctly by ChatGPT and 37.6% (50/133) of remaining incorrect questions correctly. Among 12 question categories, GPT-4 significantly outperformed users in each but performed comparably with ChatGPT in 3 (functional, other general, and spine) and outperformed both users and ChatGPT for tumor questions. Increased word count (odds ratio = 0.89 of answering a question correctly per +10 words) and higher-order problem-solving (odds ratio = 0.40, P = .009) were associated with lower accuracy for ChatGPT, but not for GPT-4 (both P > .005). Multimodal input was not available at the time of this study; hence, on questions with image content, ChatGPT and GPT-4 answered 49.5% and 56.8% of questions correctly based on contextual context clues alone. CONCLUSION: LLMs achieved passing scores on a mock 500-question neurosurgical written board examination, with GPT-4 significantly outperforming ChatGPT.

Postnatal Myelomeningocele Repair in the United States: Rates and Disparities Before and After the Management of Myelomeningocele Study Trial.

BACKGROUND AND OBJECTIVES: Evolving technologies have influenced the practice of myelomeningocele repair (MMCr), including mandatory folic acid fortification, advances in prenatal diagnosis, and the 2011 Management of Myelomeningocele Study (MOMS) trial demonstrating benefits of fetal over postnatal MMCr in select individuals. Postnatal MMCr continues to be performed, especially for those with limitations in prenatal diagnosis, health care access, anatomy, or personal preference. A comprehensive, updated national perspective on the trajectory of postnatal MMCr volumes and patient disparities is absent. We characterize national trends in postnatal MMCr rates before and after the MOMS trial publication (2000-2010 vs 2011-2019) and examine whether historical disparities persist. METHODS: This retrospective, cross-sectional analysis queried Nationwide Inpatient Sample data for postnatal MMCr admissions. Annual and race/ethnicity-specific rates were calculated using national birth registry data. Time series analysis assessed for trends relative to the year 2011. Patient, admission, and outcome characteristics were compared between pre-MOMS and post-MOMS cohorts. RESULTS: Between 2000 and 2019, 12 426 postnatal MMCr operations were estimated nationwide. After 2011, there was a gradual, incremental decline in the annual rate of postnatal MMCr. Post-MOMS admissions were increasingly associated with Medicaid insurance and the lowest income quartiles, as well as increased risk indices, length of stay, and hospital charges. By 2019, race/ethnicity-adjusted rates seemed to converge. The mortality rate remained low in both eras, and there was a lower rate of same-admission shunting post-MOMS. CONCLUSION: National rates of postnatal MMCr gradually declined in the post-MOMS era. Medicaid and low-income patients comprise an increasing majority of MMCr patients post-MOMS, whereas historical race/ethnicity-specific disparities are improving. Now more than ever, we must address disparities in the care of MMC patients before and after birth.

Performance of ChatGPT, GPT-4, and Google Bard on a Neurosurgery Oral Boards Preparation Question Bank.

BACKGROUND AND OBJECTIVES: General large language models (LLMs), such as ChatGPT (GPT-3.5), have demonstrated the capability to pass multiple-choice medical board examinations. However, comparative accuracy of different LLMs and LLM performance on assessments of predominantly higher-order management questions is poorly understood. We aimed to assess the performance of 3 LLMs (GPT-3.5, GPT-4, and Google Bard) on a question bank designed specifically for neurosurgery oral boards examination preparation. METHODS: The 149-question Self-Assessment Neurosurgery Examination Indications Examination was used to query LLM accuracy. Questions were inputted in a single best answer, multiple-choice format. χ 2 , Fisher exact, and univariable logistic regression tests assessed differences in performance by question characteristics. RESULTS: On a question bank with predominantly higher-order questions (85.2%), ChatGPT (GPT-3.5) and GPT-4 answered 62.4% (95% CI: 54.1%-70.1%) and 82.6% (95% CI: 75.2%-88.1%) of questions correctly, respectively. By contrast, Bard scored 44.2% (66/149, 95% CI: 36.2%-52.6%). GPT-3.5 and GPT-4 demonstrated significantly higher scores than Bard (both P < .01), and GPT-4 outperformed GPT-3.5 ( P = .023). Among 6 subspecialties, GPT-4 had significantly higher accuracy in the Spine category relative to GPT-3.5 and in 4 categories relative to Bard (all P < .01). Incorporation of higher-order problem solving was associated with lower question accuracy for GPT-3.5 (odds ratio [OR] = 0.80, P = .042) and Bard (OR = 0.76, P = .014), but not GPT-4 (OR = 0.86, P = .085). GPT-4's performance on imaging-related questions surpassed GPT-3.5's (68.6% vs 47.1%, P = .044) and was comparable with Bard's (68.6% vs 66.7%, P = 1.000). However, GPT-4 demonstrated significantly lower rates of "hallucination" on imaging-related questions than both GPT-3.5 (2.3% vs 57.1%, P < .001) and Bard (2.3% vs 27.3%, P = .002). Lack of question text description for questions predicted significantly higher odds of hallucination for GPT-3.5 (OR = 1.45, P = .012) and Bard (OR = 2.09, P < .001). CONCLUSION: On a question bank of predominantly higher-order management case scenarios for neurosurgery oral boards preparation, GPT-4 achieved a score of 82.6%, outperforming ChatGPT and Google Bard.