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.

The burden of unhelmeted motorcycle injury: A nationwide scoring-based analysis of helmet safety legislation.

INTRODUCTION: Motorcycle collisions comprise a large portion of motor vehicle injuries and fatalities with over 80,000 injuries and 5,500 fatalities per year in the United States. Unhelmeted riders have poor medical outcomes and generate billions in costs. Despite helmet use having been shown to lower the risk of neurological injury and death, helmet compliance is not universal, and legislation concerning helmet use also varies widely across the United States. METHODS: In this study, we systematically reviewed helmet-related statutes from all US jurisdictions. We evaluated the stringency of these statutes using a legislative scoring system termed the Helmet Safety Score (HSS) ranging from 0-7 points, with higher scores denoting more stringent statutes. Regression modeling was used to predict unhelmeted mortality using our safety scores. RESULTS: The mean score across all jurisdictions was 4.73. We found jurisdictions with higher HSS's generally had lower percentages of unhelmeted fatalities in terms of total fatalities as well as per 100,000 people and 100,000 registered motorcycles. In contrast, some lower-scoring jurisdictions had over 100 times more unhelmeted fatalities than higher-scoring jurisdictions. Our HSS significantly predicted unhelmeted motorcycle fatalities per 100,000 people (ß = -0.228 per 1-point increase, 95% CI: -0.288 to -0.169, p < .0001) and per 100,000 registered motorcycles (ß = -6.17 per 1-point increase, 95% CI: -8.37 to -3.98, p < .0001) in each state. Aspects of our score concerning helmet exemptions for riders and motorcycle-type vehicles independently predicted higher fatalities (p < .0001). Higher safety scores predicted lower unhelmeted fatalities. CONCLUSION: Stringent helmet laws may be an effective mechanism for decreasing unhelmeted mortality. Therefore, universal helmet laws may be one such mechanism to decrease motorcycle-related neurological injury and fatality burden. In states with existing helmet laws, elimination of exemptions for certain riders and motorcycle-type vehicles may also decrease fatalities.

Trends in Neurotrauma Epidemiology, Management, and Outcomes during the COVID-19 Pandemic in Kigali, Rwanda.

National regulations to curb the coronavirus disease 2019 (COVID-19) transmission and health care resource reallocation may have impacted incidence and treatment for neurotrauma, including traumatic brain injury (TBI) and spinal trauma, but these trends have not been characterized in Sub-Saharan Africa. This study analyzes differences in epidemiology, management, and outcomes preceding and during the COVID-19 pandemic for neurotrauma patients in a Rwandan tertiary hospital. The study setting was the Centre Hospitalier Universitaire de Kigali (CHUK), Rwanda's national referral hospital. Adult injury patients presenting to the CHUK Emergency Department (ED) were prospectively enrolled from January 27, 2020 to June 28, 2020. Study personnel collected data on demographics, injury characteristics, serial neurological examinations, treatment, and outcomes. Differences in patients before (January 27, 2020 to March 21, 2020) and during (June 1, 2020 to June 28, 2020) the COVID-19 pandemic were assessed using chi-squared and Mann-Whitney U tests. The study population included 216 patients with neurotrauma (83.8% TBI, 8.3% spine trauma, and 7.9% with both). Mean age was 34.1 years (standard deviation [SD] = 12.5) and 77.8% were male. Patients predominantly experienced injury following a road traffic accident (RTA; 65.7%). Weekly volume for TBI (mean = 16.5 vs. 17.1, p = 0.819) and spine trauma (mean = 2.0 vs. 3.4, p = 0.086) was similar between study periods. During the pandemic, patients had lower Glasgow Coma Scale (GCS) scores (mean = 13.8 vs. 14.3, p = 0.068) and Kampala Trauma Scores (KTS; mean = 14.0 vs. 14.3, p = 0.097) on arrival, denoting higher injury severity, but these differences only approached significance. Patients treated during the pandemic period had higher occurrence of hemorrhage, contusion, or fracture on computed tomography (CT) imaging (47.1% vs. 26.7%, p = 0.003) and neurological decline (18.6% vs. 7.5%, p = 0.016). Hospitalizations also increased significantly during COVID-19 (54.6% vs. 39.9%, p = 0.048). Craniotomy rates doubled during the pandemic period (25.7% vs. 13.7%, p = 0.003), but mortality was unchanged (5.5% vs. 5.7%, p = 0.944). Neurotrauma volume remained unchanged at CHUK during the COVID-19 pandemic, but presenting patients had higher injury acuity and craniotomy rates. These findings may inform care during pandemic conditions in Rwanda and similar settings.

The Surgical Histopathology of the Filum Terminale: Findings from a Large Series of Patients with Tethered Cord Syndrome.

This study investigated the prevalence of embryonic and connective tissue elements in the filum terminale (FT) of patients with tethered cord syndrome (TCS), examining both typical and pathological histology. The FT specimens from 288 patients who underwent spinal cord detethering from 2013 to 2021 were analyzed. The histopathological examination involved routine hematoxylin and eosin staining and specific immunohistochemistry when needed. The patient details were extracted from electronic medical records. The study found that 97.6% of the FT specimens had peripheral nerves, and 70.8% had regular ependymal cell linings. Other findings included ependymal cysts and canals, ganglion cells, neuropil, and prominent vascular features. Notably, 41% showed fatty infiltration, and 7.6% had dystrophic calcification. Inflammatory infiltrates, an underreported finding, were observed in 3.8% of the specimens. The research highlights peripheral nerves and ganglion cells as natural components of the FT, with ependymal cell overgrowth and other tissues potentially linked to TCS. Enlarged vessels may suggest venous congestion due to altered FT mechanics. The presence of lymphocytic infiltrations and calcifications provides new insights into structural changes and mechanical stress in the FT, contributing to our understanding of TCS pathology.

Navigation-assisted resection of tumoral calcinosis of the lumbosacral spine: illustrative case.

BACKGROUND: Tumoral calcinosis is an uncommon disease resulting from dystrophic calcium phosphate crystal deposition, with only 7% of cases involving the spine, and it may diagnostically mimic neoplasms. OBSERVATIONS: In this case, a 54-year-old woman with history of systemic scleroderma presented with 10 months of progressive left lumbosacral pain. Imaging revealed an expansile, 4 × 7-cm, well-circumscribed mass in the lumbosacral spine with L5-S1 neuroforaminal compression. Because intractable pain and computed tomography (CT)-guided needle biopsy did not entirely rule out malignancy, operative management was pursued. The patient underwent L4-S2 laminectomies, left L5-S1 facetectomy, L5 and S1 pediculectomies, and en bloc resection, performed under stereotactic CT-guided intraoperative navigation. Subsequently, instrumented fusion was performed with L4 and L5 pedicle screws and S2 alar-iliac screws. Pathological examination was consistent with tumoral calcinosis, with multiple nodules of amorphous basophilic granular calcified material lined by histiocytes. There was no evidence of recurrence or neurological deficits at 5-month follow-up. LESSONS: Because spinal tumoral calcinosis may mimic neoplasms on imaging or gross intraoperative appearance, awareness of this clinical entity is essential for any spine surgeon. A review of all case reports of lumbosacral tumoral calcinosis (n = 14 from 1952 to 2016) was additionally performed. The case featured in this report presents the first known case of navigation-assisted resection of lumbosacral tumoral calcinosis.