Differentiable partition function calculation for RNA.

Ribonucleic acid (RNA) is an essential molecule in a wide range of biological functions. In 1990, McCaskill introduced a dynamic programming algorithm for computing the partition function of an RNA sequence. McCaskill's algorithm is widely used today for understanding the thermodynamic properties of RNA. In this work, we introduce a generalization of McCaskill's algorithm that is well-defined over continuous inputs. Crucially, this enables us to implement an end-to-end differentiable partition function calculation. The derivative can be computed with respect to the input, or to any other fixed values, such as the parameters of the energy model. This builds a bridge between RNA thermodynamics and the tools of differentiable programming including deep learning as it enables the partition function to be incorporated directly into any end-to-end differentiable pipeline. To demonstrate the effectiveness of our new approach, we tackle the inverse folding problem directly using gradient optimization. We find that using the gradient to optimize the sequence directly is sufficient to arrive at sequences with a high probability of folding into the desired structure. This indicates that the gradients we compute are meaningful.

Fitness functions for RNA structure design.

An RNA design algorithm takes a target RNA structure and finds a sequence that folds into that structure. This is fundamentally important for engineering therapeutics using RNA. Computational RNA design algorithms are guided by fitness functions, but not much research has been done on the merits of these functions. We survey current RNA design approaches with a particular focus on the fitness functions used. We experimentally compare the most widely used fitness functions in RNA design algorithms on both synthetic and natural sequences. It has been almost 20 years since the last comparison was published, and we find similar results with a major new result: maximizing probability outperforms minimizing ensemble defect. The probability is the likelihood of a structure at equilibrium and the ensemble defect is the weighted average number of incorrect positions in the ensemble. We find that maximizing probability leads to better results on synthetic RNA design puzzles and agrees more often than other fitness functions with natural sequences and structures, which were designed by evolution. Also, we observe that many recently published approaches minimize structure distance to the minimum free energy prediction, which we find to be a poor fitness function.

RNA design via structure-aware multifrontier ensemble optimization.

MOTIVATION: RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. RESULTS: We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. AVAILABILITY AND IMPLEMENTATION: Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.

Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.

MOTIVATION: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem. RESULTS: We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/marcellszi/dl-rna. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Complications associated with deep brain stimulation for Parkinson's disease: a MAUDE study.

INTRODUCTION: Deep brain stimulation (DBS) is a common surgical option for the treatment of medically refractory Parkinson's disease (PD). Manufacturer and User Facility Device Experience (MAUDE), a United States Food and Drug Administration (FDA)-compiled database of adverse event reports related to medical devices, is a public resource that can provide insight into the relative frequency of complications and patient complaints. MATERIALS AND METHODS: We accessed the MAUDE database and queried for adverse reports for deep brain stimulators implanted for PD from January 1, 2009 to December 31, 2018. Complaints were classified into device malfunction, patient non-compliance, patient complaint, surgically managed complications (i.e. complications that are corrected via surgery), and death. Patient complaints were further stratified into ineffective stimulation, shock, overstimulation, battery-related problems, or pain at the pulse generator site. Surgically managed complications were classified as intraoperative complications, impedance, migration, erosion, infection, lead fracture, and lead disconnection. Each event could receive multiple classifications and subclassifications. RESULTS: A total of 4,189 adverse event reports was obtained. These encompassed 2,805 patient complaints. Within this group, 797 (28%) events were classified as ineffective stimulation. There were 1,382 surgically managed complications, 104 (8%) of which were intraoperative complications, 757 (55%) documented impedance issues, 381 (28%) infections, and 413 (30%) lead-related issues. There were 53 documented deaths. CONCLUSIONS: The MAUDE database has potential use as a real time monitor for elucidating the relative occurrence of complications associated with deep brain stimulation. It also allows for the analysis of device-related complications in specific patient populations. Although the database is useful in this endeavor, it requires improvements particularly in the standardization of reporting adverse events.

Determining parameters for non-linear models of multi-loop free energy change.

MOTIVATION: Predicting the secondary structure of RNA is a fundamental task in bioinformatics. Algorithms that predict secondary structure given only the primary sequence, and a model to evaluate the quality of a structure, are an integral part of this. These algorithms have been updated as our model of RNA thermodynamics changed and expanded. An exception to this has been the treatment of multi-loops. Although more advanced models of multi-loop free energy change have been suggested, a simple, linear model has been used since the 1980s. However, recently, new dynamic programing algorithms for secondary structure prediction that could incorporate these models were presented. Unfortunately, these models appear to have lower accuracy for secondary structure prediction. RESULTS: We apply linear regression and a new parameter optimization algorithm to find better parameters for the existing linear model and advanced non-linear multi-loop models. These include the Jacobson-Stockmayer and Aalberts & Nandagopal models. We find that the current linear model parameters may be near optimal for the linear model, and that no advanced model performs better than the existing linear model parameters even after parameter optimization. AVAILABILITY AND IMPLEMENTATION: Source code and data is available at https://github.com/maxhwardg/advanced_multiloops. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Endoscopic Infraorbital Microdissection for Localized V2 Trigeminal Neuralgia.

Trigeminal neuralgia is a chronic and debilitating syndrome characterized by short paroxysms of lancinating facial pain. Patients may be medically managed; however, in cases of medically refractory trigeminal neuralgia, surgical management is often required. Our objective was to present and describe a technique for endoscopic microdissection of the infraorbital nerve, a peripheral method of management for refractory V2 trigeminal neuralgia in patients without evidence of neurovascular compression. The technique is designed to spare sensation in unaffected portions of the V2 distribution. We present 2 patients with medically refractory V2 trigeminal neuralgia localized to the lateral midface who underwent infraorbital microdissection. After first confirming that there was no neurovascular compression on imaging in these patients, we administered infraorbital bupivacaine injections to localize the symptomatic nerve. The nerve was then accessed via a 1.5-cm buccogingival incision, and the connective tissue sheath was incised. The nerve fascicles were bluntly separated, and the symptomatic branches were cauterized with fine-tipped monopolar cautery. Both patients reported complete resolution of their pain postoperatively and were pain free at last follow-up. They reported some hypoesthesia in the lateral face; however, they retained some sensation in the medial upper lip, midface, and nose. Infraorbital microdissection is a safe and effective technique for symptomatic management of V2 trigeminal neuralgia while sparing sensation in asymptomatic portions of the dermatome.

Advanced multi-loop algorithms for RNA secondary structure prediction reveal that the simplest model is best.

Algorithmic prediction of RNA secondary structure has been an area of active inquiry since the 1970s. Despite many innovations since then, our best techniques are not yet perfect. The workhorses of the RNA secondary structure prediction engine are recursions first described by Zuker and Stiegler in 1981. These have well understood caveats; a notable flaw is the ad-hoc treatment of multi-loops, also called helical-junctions, that persists today. While several advanced models for multi-loops have been proposed, it seems to have been assumed that incorporating them into the recursions would lead to intractability, and so no algorithms for these models exist. Some of these models include the classical model based on Jacobson-Stockmayer polymer theory, and another by Aalberts and Nadagopal that incorporates two-length-scale polymer physics. We have realized practical, tractable algorithms for each of these models. However, after implementing these algorithms, we found that no advanced model was better than the original, ad-hoc model used for multi-loops. While this is unexpected, it supports the praxis of the current model.

Investigating Complications Associated With Occipital Nerve Stimulation: A MAUDE Study.

OBJECTIVES: The objectives of this study are to utilize the MAUDE data base to enhance our understanding of the complication profile for Occipital Nerve Stimulation, a therapy for which the current level of evidence is limited. Additionally, it is our objective to describe a systematic approach to processing the MAUDE data, which addresses its flaws and enhances its utility. METHODS: From the FDA website, we accessed adverse events reports from the MAUDE data base for devices used in Occipital Nerve Stimulation between June 30, 2007 and June 30, 2017. All reports were sorted into an overall classification for types of adverse events, types of patient complaints, and types of specific device-related complications. We then evaluated for the total number of adverse event reports that contained each of the patient complains and device-related complications. RESULTS: A total of 1233 adverse event records were obtained. Eight hundred twenty-two records were classified as surgically manageable post-operative complications, 121 as device malfunction, 29 as patient compliance issues, and 27 as intra-operative complications. Two hundred thirty-seven records were not classified. A total of 683 records contained patient complaints including 467 complaints of ineffective stimulation, 122 complaints of inappropriate or over-stimulation, 50 complaints of device-shock, and 44 complaints of IPG site pain. We found 581 post-operative device-related complications, which included 206 instances of lead migration, 157 reports of lead erosion, 155 infections, 46 lead-fractures, and 17 lead disconnections. CONCLUSION: The MAUDE data base is a useful tool to investigate device related complications and helps fill the current gap in ONS data. Reviewing the types and frequencies of complications reported over the years allows clinicians with less personal experience to have a more realistic expectation of complications and make informed decisions based on the patient's unique needs. Additionally, patient complaint data are useful in establishing more realistic expectations for patient outcomes.

Google Trends as a Resource for Informing Plastic Surgery Marketing Decisions.

BACKGROUND: Celebrities have long influenced the medical decisions of the general population. By analyzing Google search data using Google Trends, we measured the impact of highly publicized plastic surgery-related events on the interest level of the general population in specific search terms. Additionally, we investigated seasonal and geographic trends around interest in rhinoplasties, which is information that physicians and small surgical centers can use to optimize marketing decisions. METHODS: Google Trends was used to access search data histories for three separate areas of interest: Kylie Jenner and lip fillers, Joan Rivers and plastic surgery, and rhinoplasty, which were then analyzed using two-tailed, two-sample equal variance t-tests. RESULTS: The average interest level in fillers increased by 30.31 points after Kylie Jenner announced that she received Juvéderm lip injections. The interest level in plastic surgery was decreased by 21.3% the month after Joan Rivers' death. Between January 2004 and May 2017, the average interest level for rhinoplasty was significantly different in January/December (67.91 ± 20.68) and June/July (70.12 ± 18.89) from the remaining calendar months (63.58 ± 19.67). Los Angeles, New York City, and Miami showed consistently high interest levels throughout the time period, while Tulsa, OK, showed a major interest increase between 2015 to 2016 of 65 points. CONCLUSIONS: A noticeable impact was observed in both celebrity cases on search term volume, and a seasonal effect is apparent for rhinoplasty searches. As many surgeons already employ aggressive Internet marketing strategies, understanding and utilizing these trends could help optimize their investments, increase social engagement, and increase practice awareness by potential patients. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Deep Brain Stimulation for the Treatment of Dejerine-Roussy Syndrome.

BACKGROUND/AIMS: Patients who suffer from Dejerine-Roussy syndrome commonly experience severe poststroke hemibody pain which has historically been attributed to thalamic lesions. Despite pharmacological treatment, a significant proportion of the population is resistant to traditional therapy. Deep brain stimulation is often appropriate for the treatment of resistant populations. In this review we aim to summarize the targets that are used to treat Dejerine-Roussy syndrome and provide insight into their clinical efficacy. METHODS: In reviewing the literature, we defined stimulation success as achievement of a minimum of 50% pain relief. RESULTS: Contemporary targets for deep brain stimulation are the ventral posterior medial/ventral posterior lateral thalamic nuclei, periaqueductal/periventricular gray matter, the ventral striatum/anterior limb of the internal capsule, left centromedian thalamic nuclei, the nucleus ventrocaudalis parvocellularis internis, and the posterior limb of the internal capsule. CONCLUSIONS: Due to technological advancements in deep brain stimulation, its therapeutic effects must be reevaluated. Despite a lack of controlled evidence, deep brain stimulation has been effectively used as a therapeutic in clinical pain management. Further clinical investigation is needed to definitively evaluate the therapeutic efficacy of deep brain stimulation in treating the drug-resistant patient population.

A Bibliometric Analysis of the 20 Most Cited Articles on Sacrococcygeal Chordomas.

This study aims to summarize sacrococcygeal chordoma literature through bibliometric analysis and to offer insights into key studies to guide clinical practices and future research. The Web of Science database was searched using the terms "sacral chordoma", "chordomas of the sacrum", "chordomas of the sacral spine", "chordomas of the sacrococcygeal region", "coccygeal chordoma", and "coccyx chordoma". Articles were analyzed for citation count, authorship, publication date, journal, research area tags, impact factor, and evidence level. The median number of citations was 75 (range: 53-306). The primary publication venue was the International Journal of Radiation Oncology, Biology, Physics. Most works, published between 1999 and 2019, featured a median journal impact factor of 3.8 (range: 2.1-7) and predominantly fell under the research area tag, radiation, nuclear medicine, and imaging. Of these articles, 19 provided clinical data with predominantly level III evidence, and one was a literature review. This review highlights the increasing volume of sacrococcygeal chordoma publications over the past two decades, indicating evolving treatment methods and interdisciplinary patient care. Advances in radiation, particularly intensity-modulated radiation therapy (IMRT) and proton beam therapy, are believed to be propelling research growth, and the lack of level I evidence underscores the need for more rigorous studies to refine treatment protocols for sacrococcygeal chordomas.

Enhancing Diagnostic Support for Chiari Malformation and Syringomyelia: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The rapidly increasing adoption of large language models in medicine has drawn attention to potential applications within the field of neurosurgery. This study evaluates the effects of various contextualization methods on ChatGPT's ability to provide expert-consensus aligned recommendations on the diagnosis and management of Chiari Malformation and Syringomyelia. METHODS: Native GPT4 and GPT4 models contextualized using various strategies were asked questions revised from the 2022 Chiari and Syringomyelia Consortium International Consensus Document. ChatGPT-provided responses were then compared to consensus statements using reviewer assessments of 1) responding to the prompt, 2) agreement of ChatGPT response with consensus statements, 3) recommendation to consult with a medical professional, and 4) presence of supplementary information. Flesch-Kincaid, SMOG, word count, and Gunning-Fog readability scores were calculated for each model using the quanteda package in R. RESULTS: Relative to GPT4, all contextualized GPTs demonstrated increased agreement with consensus statements. PDF+Prompting and Prompting models provided the most elevated agreement scores of 19 of 24 and 23 of 24, respectively, versus 9 of 24 for GPT4 (p=.021, p=.001). A trend toward improved readability was observed when comparing contextualized models at large to ChatGPT4, with significant decreases in average word count (180.7 vs 382.3, p<.001) and Flesch-Kincaid Reading Ease score (11.7 vs 17.2, p=.033). CONCLUSIONS: The enhanced performance observed in response to ChatGPT4 contextualization suggests broader applications of large language models in neurosurgery than what the current literature indicates. This study provides proof of concept for the use of contextualized GPT models in neurosurgical contexts and showcases the easy accessibility of improved model performance.

A Quantitative Assessment of ChatGPT as a Neurosurgical Triaging Tool.

BACKGROUND AND OBJECTIVES: ChatGPT is a natural language processing chatbot with increasing applicability to the medical workflow. Although ChatGPT has been shown to be capable of passing the American Board of Neurological Surgery board examination, there has never been an evaluation of the chatbot in triaging and diagnosing novel neurosurgical scenarios without defined answer choices. In this study, we assess ChatGPT's capability to determine the emergent nature of neurosurgical scenarios and make diagnoses based on information one would find in a neurosurgical consult. METHODS: Thirty clinical scenarios were given to 3 attendings, 4 residents, 2 physician assistants, and 2 subinterns. Participants were asked to determine if the scenario constituted an urgent neurosurgical consultation and what the most likely diagnosis was. Attending responses provided a consensus to use as the answer key. Generative pretraining transformer (GPT) 3.5 and GPT 4 were given the same questions, and their responses were compared with the other participants. RESULTS: GPT 4 was 100% accurate in both diagnosis and triage of the scenarios. GPT 3.5 had an accuracy of 92.59%, slightly below that of a PGY1 (96.3%), an 88.24% sensitivity, 100% specificity, 100% positive predictive value, and 83.3% negative predicative value in triaging each situation. When making a diagnosis, GPT 3.5 had an accuracy of 92.59%, which was higher than the subinterns and similar to resident responders. CONCLUSION: GPT 4 is able to diagnose and triage neurosurgical scenarios at the level of a senior neurosurgical resident. There has been a clear improvement between GPT 3.5 and 4. It is likely that the recent updates in internet access and directing the functionality of ChatGPT will further improve its utility in neurosurgical triage.

Ethical Incorporation of Artificial Intelligence into Neurosurgery: A Generative Pretrained Transformer Chatbot-Based, Human-Modified Approach.

INTRODUCTION: Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pretrained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain underexplored. We delineate key ethical considerations using a novel GPT-based, human-modified approach, synthesize the most common considerations, and present an ethical framework for the involvement of AI in neurosurgery. METHODS: GPT-4, ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can artificial intelligence be ethically incorporated into neurosurgery?". Then, a layered GPT-based thematic analysis was performed. The authors synthesized the results into considerations for the ethical incorporation of AI into neurosurgery. Separate Pareto analyses with 20% threshold and 10% threshold were conducted to determine salient themes. The authors refined these salient themes. RESULTS: Twelve key ethical considerations focusing on stakeholders, clinical implementation, and governance were identified. Refinement of the Pareto analysis of the top 20% most salient themes in the aggregated GPT outputs yielded 10 key considerations. Additionally, from the top 10% most salient themes, 5 considerations were retrieved. An ethical framework for the use of AI in neurosurgery was developed. CONCLUSIONS: It is critical to address the ethical considerations associated with the use of AI in neurosurgery. The framework described in this manuscript may facilitate the integration of AI into neurosurgery, benefitting both patients and neurosurgeons alike. We urge neurosurgeons to use AI only for validated purposes and caution against automatic adoption of its outputs without neurosurgeon interpretation.

Organizing a Regional In-Person Medical Student Symposium in Neuroscience and Neurosurgery Research: A How-To Guide.

BACKGROUND: Medical students are increasingly seeking out research opportunities to build their skills and network with future colleagues. Medical student-led conferences are an excellent endeavor to achieve this goal. METHODS: The American Association of Neurological Surgeons (AANS) student chapter at the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell designed an in-person medical student research symposium alongside the Northwell Health Department of Neurosurgery. Post-conference feedback forms were sent out digitally to student attendees to evaluate event planning and execution and responses were given on a scale of 1-5 (5 being the highest score). RESULTS: In December 2023, the Northeast Medical Student Research Symposium (NMSRS) was held with over 80 participants and 52 medical student presenters. Keynote speakers delivered lectures geared towards students interested in neurosurgery and neuroscience research, followed by an interactive poster board session and resident/attending networking dinner. After the conference, students affirmed that they learned more about neuroscience research after the event (mean: 4.3), were more inclined to attend other neuroscience research events in the future (mean: 4.7), and would attend this event if held next year (mean: 4.8). The poster sessions (mean: 4.75) and keynote lectures (mean: 5) were the highest rated events, while the resident/attending networking dinner (mean: 3.6) was a potential area for improvement. CONCLUSION: Regional in-person conferences are an excellent way to foster interest in neurosurgery and neuroscience research, network with like-minded peers, and prepare students for presentations at national meetings.

Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment.

Heart failure is associated with a rehospitalisation rate of up to 50% within six months. Elevated central venous pressure may serve as an early warning sign. While invasive procedures are used to measure central venous pressure for guiding treatment in hospital, this becomes impractical upon discharge. A non-invasive estimation technique exists, where the clinician visually inspects the pulsation of the jugular veins in the neck, but it is less reliable due to human limitations. Video and signal processing technologies may offer a high-fidelity alternative. This state-of-the-art review analyses existing literature on camera-based methods for jugular vein assessment. We summarize key design considerations and suggest avenues for future research. Our review highlights the neck as a rich imaging target beyond the jugular veins, capturing comprehensive cardiac signals, and outlines factors affecting signal quality and measurement accuracy. Addressing an often quoted limitation in the field, we also propose minimum reporting standards for future studies.

Exploring the Intersection of Artificial Intelligence and Neurosurgery: Let us be Cautious With ChatGPT.

BACKGROUND AND OBJECTIVES: ChatGPT is a novel natural language processing artificial intelligence (AI) module where users enter any question or command and receive a single text response within seconds. As AI becomes more accessible, patients may begin to use it as a resource for medical information and advice. This is the first study to assess the neurosurgical information that is provided by ChatGPT. METHODS: ChatGPT was accessed in January 2023, and prompts were created requesting treatment information for 40 common neurosurgical conditions. Quantitative characteristics were collected, and four independent reviewers evaluated the responses using the DISCERN tool. Prompts were compared against the American Association of Neurological Surgeons (AANS) "For Patients" webpages. RESULTS: ChatGPT returned text organized in paragraph and bullet-point lists. ChatGPT responses were shorter (mean 270.1 ± 41.9 words; AANS webpage 1634.5 ± 891.3 words) but more difficult to read (mean Flesch-Kincaid score 32.4 ± 6.7; AANS webpage 37.1 ± 7.0). ChatGPT output was found to be of "fair" quality (mean DISCERN score 44.2 ± 4.1) and significantly inferior to the "good" overall quality of the AANS patient website (57.7 ± 4.4). ChatGPT was poor in providing references/resources and describing treatment risks. ChatGPT provided 177 references, of which 68.9% were inaccurate and 33.9% were completely falsified. CONCLUSION: ChatGPT is an adaptive resource for neurosurgical information but has shortcomings that limit the quality of its responses, including poor readability, lack of references, and failure to fully describe treatment options. Hence, patients and providers should remain wary of the provided content. As ChatGPT or other AI search algorithms continue to improve, they may become a reliable alternative for medical information.

Characterizing complications associated with skull clamps: A review of the manufacturer and user facility device experience database.

OBJECTIVE: Head immobilization with skull clamps is a prerequisite of many neurosurgical procedures. Adverse events relating to the use of skull clamps have been reported, however, given the paucity of published reports, we sought to conduct a more comprehensive analysis using the Manufacturer and User Facility Device Experience (MAUDE) database. METHODS: The MAUDE database was queried for neurosurgical skull clamp events over a 10-year period between January 2013 and December 2022. Reports were qualitatively analyzed and categorically assigned by the study authors as 'mechanical failure,' 'slippage,' 'contamination,' 'insufficient information,' and 'other.' Patient injury reports were also classified as 'abrasion,' 'laceration,' 'hematoma,' 'fracture,' 'intracranial hemorrhage (ICH),' 'other,' 'insufficient information,' and 'death.' RESULTS: Of 3672 reports retrieved, 2689 (73.2%) were device malfunctions, with mechanical failure (50.7%) and slippage (47.7%) being the most common causes. There were 983 reports (26.8%) involving patient injury which included lacerations (n = 776, 78.9%), fractures (n = 24, 2.4%), abrasions (n = 23, 2.3%), hematomas (n = 7, 0.71%), ICH (n = 3, 0.31%), and other causes (n = 6, 0.61%). Five (0.1%) deaths due to skull clamp related complications were also reported. CONCLUSIONS: This study provides a more comprehensive picture of adverse events in neurosurgical procedures relating to the use of skull clamps. Mechanical failures of device parts were the most common device-related complication, and lacerations the most common adverse patient-related event. While more severe patient-related events were reported, they are relatively rare. The MAUDE database is useful for characterizing underreported device-related and patient-related adverse events.