More clinicians and researchers are exploring uses for large language model chatbots, such as ChatGPT, for research, dissemination, and educational purposes. Therefore, it becomes increasingly relevant to consider the full potential of this tool, including the special features that are currently available through the application programming interface. One of these features is a variable called temperature, which changes the degree to which randomness is involved in the model’s generated output. This is of particular interest to clinicians and researchers. By lowering this variable, one can generate more consistent outputs; by increasing it, one can receive more creative responses. For clinicians and researchers who are exploring these tools for a variety of tasks, the ability to tailor outputs to be less creative may be beneficial for work that demands consistency. Additionally, access to more creative text generation may enable scientific authors to describe their research in more general language and potentially connect with a broader public through social media. In this viewpoint, we present the temperature feature, discuss potential uses, and provide some examples.

Fragile X Syndrome (FXS), the leading monogenic cause of intellectual disability and autism spectrum disorder, is caused by expansion of a CGG trinucleotide repeat in the 5ʹ-UTR of the Fragile X Mental Retardation-1 (FMR1) gene. Epigenetic silencing of FMR1 results in loss of the Fragile X Mental Retardation Protein (FMRP). Although most studies to date have focused on excitatory neurons, recent evidence suggests that GABAergic inhibitory networks are also affected. To investigate human GABAergic neurogenesis, we established a method to reproducibly derive inhibitory neurons from multiple FXS and control human pluripotent stem cell (hPSC) lines. Electrophysiological analyses suggested that the developing FXS neurons had a delay in the GABA functional switch, a transition in fetal development that converts the GABA_A channel’s function from depolarization to hyperpolarization, with profound effects on the developing brain. To investigate the cause of this delay, we analyzed 14 400 single-cell transcriptomes from FXS and control cells at 2 stages of GABAergic neurogenesis. While control and FXS cells were similar at the earlier time point, the later-stage FXS cells retained expression of neuroblast proliferation-associated genes and had lower levels of genes associated with action potential regulation, synapses, and mitochondria compared with controls. Our analysis suggests that loss of FMRP prolongs the proliferative stage of progenitors, which may result in more neurons remaining immature during the later stages of neurogenesis. This could have profound implications for homeostatic excitatory-inhibitory circuit development in FXS, and suggests a novel direction for understanding disease mechanisms that may help to guide therapeutic interventions.

PURPOSE: The purpose of our study was to (1) use a large language model (LLM) to identify goals-of-care (GOC) conversations in a large volume of notes, and (2) explore the potential of LLMs for a novel summarization task. METHODS: We included patients diagnosed with advanced cancer between April 1, 2024, and June 30, 2024. A validated LLM prompt for GOC was applied to electronic health records (EHRs) using a Health Insurance Portability and Accountability Act (HIPPA)-secure version of GPT-4o, a LLM developed by OpenAI. Output included (1) presence or absence of GOC documentation, (2) explanations with source text used to inform the LLM's determination, and (3) a hallucination score, indicating proportion of source text generated by the LLM that did not perfectly match text in the EHR. Two LLM prompts were designed to generate structured and unstructured GOC summaries. We randomly selected five patients and applied the summarization task to notes flagged by LLM as containing GOC. We reviewed LLM summaries to examine for relevant information. RESULTS: Among 326 patients associated with nearly 1,400 clinical notes, LLM flagged approximately 40% of notes for GOC documentation. Subsequent review of explanation text identified that 128 patients (nearly 40% of the total patient population) had GOC documentation. The hallucination index for explanations was low, suggesting that the LLM did not produce text that was not found in EHRs. LLM prompts produced accurate summaries in less than 2 minutes per patient. CONCLUSION: LLMs can capture GOC at scale and generate clinically useful summaries. Future directions include real-time implementation in the clinical setting.

Background: The occurrence (incidence or prevalence) of delirium in brain tumors is unknown, yet delirium is associated with increased morbidity and mortality and worse quality of life. We conducted a systematic review and meta-analysis to determine the occurrence of delirium in hospitalized patients with brain tumors. Methods: PubMed, Scopus, and Web of Science were systematically searched for papers from 1 January 1999 to 12 July 2024, including references from texts. Cross-sectional, prospective, and other cohort study designs were included, and individual case reports, case series, editorials, and reviews were excluded. The included papers were scored using a validated sensitivity analysis tool and tested for quality and bias using funnel plots and Egger’s test. We used random effects models for the summary estimates. We performed subgroup analyses by tumor type, tumor location, delirium subtype, and length of stay. Results: Of the 452 studies screened, 27 were included, representing 35,958 patients. The overall occurrence of delirium was 0.17 (95% CI [0.11–0.24]). Delirium occurrence in patients with low-grade gliomas, high-grade gliomas, and brain metastases was 0.10 [0.06–0.16], 0.21 [0.10–0.40], and 0.31 [0.16–0.50], respectively. Compared to the occipital lobe, there was a higher occurrence of delirium for tumors in the frontal (RR 3.08 [1.35–8.22]) and temporal lobes (RR 2.88 [1.22–7.93]). The patients were more likely to have hypoactive (RR 1.61 [1.30; 1.98]) than hyperactive delirium. Delirium was associated with 4.62 additional hospitalized days compared to those without delirium (CI [3.23–6.01]). Discussion: We confirmed high occurrence rates of delirium in patients hospitalized with brain tumors. Patients with brain metastases had a higher occurrence of delirium compared to patients with gliomas, and delirium occurrence rates were higher in patients with frontotemporal tumors. Delirium occurrence rates in the literature are very heterogeneous and point toward a need for tailored assessments in patients with brain tumors.