Citations

Abstract Background Cardiac magnetic resonance elastography (MRE) allows the non-invasive quantification of myocardial stiffness, but long-interval reproducibility data at 3.0 T single-frequency 140-Hz remain limited. Purpose To evaluate the short- and long-interval reproducibility of 3.0 T cardiac MRE for left ventricular (LV) myocardial stiffness across healthy volunteers and patients with conditions associated with elevated myocardial stiffness. Materials and Methods This prospective single-center study included 69 participants: 60 healthy volunteers and 9 with cardiovascular disease (6 with aortic stenosis and 3 with long-standing hypertension). All participants underwent baseline cardiac MRE (scanner: Signa Premier 3.0 T, GE Healthcare, driver: Resoundant), an immediate short-interval rescan (SIR) within 5 minutes, and a long-interval rescan (LIR) after approximately 1 month. Left ventricular myocardial stiffness and octahedral shear strain signal-to-noise ratio were measured independently by 2 readers. Reproducibility was assessed using Bland-Altman analysis, intraclass correlation coefficients (ICCs), within-subject coefficient of variation, standard error of measurement, and smallest detectable change at the 95% confidence (SDC95) level. Results Baseline LV myocardial stiffness was higher in the disease group than in healthy volunteers, 11.73 ± 1.01 kPa and 7.80 ± 0.70 kPa (mean ± SD; P < .001), respectively. Inter-reader agreement was excellent across all timepoints, with ICCs of 0.970 at baseline (95% CI, 0.953-0.982), 0.966 for SIR (95% CI, 0.945-0.979), and 0.962 for LIR (95% CI, 0.939-0.976). Within-reader scan-rescan reproducibility was also excellent. For Reader 1, stiffness ICC was 0.979 for the SIR (95% CI, 0.966-0.987) and 0.957 for the LIR (95% CI, 0.932-0.973), with SDC95 values of 0.616 kPa (95% CI, 0.528-0.741) and 0.869 kPa (95% CI, 0.744-1.044), respectively. Reader 2 showed excellent reproducibility, with ICCs ≤0.943. Conclusions 3D single-frequency 140-Hz cardiac MRE at 3.0 T provides excellent short- and long-interval reproducibility for LV myocardial stiffness quantification. The SDC95 values may help distinguish true longitudinal stiffness changes from measurement variability.

e16017 Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and remains one of the deadliest malignancies worldwide, largely due to late-stage diagnosis. Early-stage PDAC is frequently asymptomatic, limiting opportunities for timely detection and intervention. We aimed to develop prognostic models using routinely acquired pre-diagnostic clinical data to identify individuals at high risk for PDAC prior to clinical manifestation. Methods: We conducted a retrospective study of patients treated at Mayo Clinic sites in Rochester, MN; Phoenix, AZ; and Jacksonville, FL between January 2011 and February 2023. The cohort included 1,151 patients with PDAC and 3,745 noncancer controls. CT scans were standardized to axial abdominal acquisitions with slice thickness between 0.8–3.0 mm. Radiology reports were de-identified, normalized, and parsed into structured sections (Indications, Findings, Impression), with an additional pancreas-specific section aggregating pancreas-related sentences. Demographic and clinical variables, e.g., age, sex, race, etc., were encoded using a templated sentence representation. All textual inputs were embedded using a Sentence-BERT model. A multimodal deep learning survival model was developed to integrate CT imaging, radiology report embeddings, and demographic features. The model was trained using a negative log-likelihood loss to estimate patient-specific PDAC risk scores. Model performance was assessed using the concordance index (C-index). Internal validation included patients from MN and AZ sites, while external validation used the FL cohort. Results: We evaluated unimodal (imaging, text, and demographics) and multimodal inputs. Fusion of imaging, radiology report, and demographic features achieved the highest performance, with internal and external validation C-indices of 0.885 (95% CI, 0.856–0.908) and 0.751 (95% CI, 0.719–0.783), respectively. Subgroup analysis by tumor location demonstrated differential performance. For pancreatic head tumors, internal and external C-indices were 0.916 (95% CI, 0.841–0.960) and 0.778 (95% CI, 0.730–0.823). For pancreatic body tumors, C-indices were 0.954 (95% CI, 0.897–0.997) internally and 0.929 (95% CI, 0.897–0.953) externally. For pancreatic tail tumors, internal and external C-indices were 0.903 (95% CI, 0.830–0.974) and 0.789 (95% CI, 0.689–0.874). Conclusions: A multimodal deep survival model leveraging pre-diagnostic CT imaging, radiology report embeddings, and demographic data can effectively identify individuals at high risk for PDAC prior to clinical diagnosis. Differences in performance were observed across pancreatic head, body, and tail tumors. This approach highlights the potential of opportunistic cancer risk stratification from routine imaging to enable earlier detection and targeted intervention strategies.

Abstract Objective To evaluate whether postoperative intracranial pressure–lowering interventions reduce recurrence of lateral skull base spontaneous cerebrospinal fluid leaks after surgical repair. Data Sources Ovid MEDLINE, Embase, Cochrane CENTRAL, Web of Science Core Collection, and Scopus were searched from inception through April 7, 2025. Review Methods The review followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines and included studies of adults with lateral skull base spontaneous cerebrospinal fluid leaks undergoing primary repair where postoperative intracranial pressure–lowering interventions were specified. Pooled recurrence rates were calculated by intervention type and compared with cohorts that did not receive intracranial pressure–lowering therapy. Results Thirty‐six studies including 687 patients (737 cases) met inclusion criteria. Six comparative cohorts (34 treated, 155 untreated) showed no significant difference in recurrence (risk ratio 1.16, 95% confidence interval [CI]: 0.14–9.76; I 2 = 0%). Across 82 intervention‐only cases, recurrence occurred in three (pooled proportion 0.10, 95% CI: 0.04–0.26; I 2 = 0%). Among 650 control‐only cases, 45 recurred (pooled proportion 0.09, 95% CI: 0.07–0.12; I 2 = 0%). No heterogeneity was observed across pooled analyses. Conclusion Available evidence does not support universal postoperative intracranial pressure–lowering therapy after lateral skull base spontaneous cerebrospinal fluid leak repair. Limited observational data suggest acetazolamide may be associated with lower recurrence, whereas lumbar drainage has not demonstrated sustained benefit. Given the heterogeneity in patient selection and intervention practices, postoperative management should be individualized. Future prospective studies are needed to better define the role of targeted therapy in high‐risk patients.

Abstract Background Community-acquired pneumonia (CAP) is a significant public health concern associated with increased rates of hospital admissions and mortality. The purpose of the current study was to compare beta-lactam plus azithromycin or doxycycline vs fluoroquinolones in adults hospitalized with CAP. Methods We searched several databases from inception to 10 June 2024. Two reviewers independently screened, selected, and extracted data. Disagreements were resolved by consensus or a third reviewer. Study quality was assessed in duplicate with the Cochrane Risk of Bias 2 for randomized studies and the Newcastle-Ottawa Scale for nonrandomized studies. Meta-analysis was conducted by a random effect model when feasible. Network meta-analysis was performed to compare direct and indirect evidence. The GRADE approach (Grading of Recommendations Assessment, Development, and Evaluation) was followed to rate the certainty of evidence. Results Twenty studies were included (20 beta-lactam + azithromycin, 7 beta-lactam + doxycycline, 12 levofloxacin, and 4 moxifloxacin): 6 were randomized, enrolling 861 participants (mean age, 61.8 years; 43.2% women), and 14 were nonrandomized, enrolling 185 928 participants (mean age, 65.2 years; 42.9% women). Direct comparison and network meta-analysis results showed no statistically significant differences and likely equal effectiveness between beta-lactam + azithromycin and beta-lactam + doxycycline in terms of in-hospital mortality, need for intensive care unit admission or invasive mechanical ventilation, or safety outcomes including Clostridioides difficile infection and QT prolongation. Conclusions The current evidence demonstrated no likely difference in outcomes between azithromycin and doxycycline in adults hospitalized with CAP receiving beta-lactam. Head-to-head randomized clinical trials are needed to validate these results.

Objectives This study aims to compare the reliability and accuracy of three large language models (LLMs) (Claude, Gemini and GPT) in assessing the risk of bias of nonrandomised studies using the ROBINS-I tool. Methods and analysis We conducted a secondary analysis of 171 nonrandomised studies previously assessed with Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool by two independent human review teams. Only studies with concordant human domain-level ratings were included. Each study was independently assessed twice by Claude, Gemini and Generative Pre-trained Transformer (GPT) using agent-based structured implementations of the ROBINS-I tool. Reliability (agreement between two runs of the same LLM) was evaluated using percent agreement and Gwet’s AC1. Accuracy (agreement with human reviewers) was assessed only for studies with consistent LLM ratings, using the same metrics. Results Claude demonstrated high reliability across all domains (79.5–98.0% agreement, AC1=0.729–0.975). Gemini showed moderate-to-high reliability (agreement 76.7–100%, AC1=0.680–1.0). GPT exhibited lower reliability overall, though domain-level agreement ranged from 70.9–95.6% (AC1=0.596–0.944). In terms of accuracy, Claude showed overall poor agreement with human reviewers (14.4–68.5% agreement; low AC1 values). Gemini demonstrated moderate-to-high accuracy in several domains, including deviations from intended interventions (79.6%, AC1=0.848) and measurement of outcomes (73.9%, AC1=0.702), with the highest overall agreement (40.0%, AC1=0.672). GPT showed variable accuracy, with the highest in measurement of outcomes (62.8%, AC1=0.571) and classification of interventions (57.8%, AC1=0.498), but poor performance in selection (14.3%, AC1 = −0.041) and overall agreement (23.0%, AC1=0.267). Conclusions Claude was internally consistent but poorly aligned with human reviewers. Gemini achieved both high reliability and moderate-to-high accuracy, whereas GPT had lower reliability and mixed accuracy. Current off-the-shelf LLMs cannot reliably perform ROBINS-I risk of bias assessments.

Abstract Background Atypical hemolytic uremic syndrome (aHUS) is a rare, complement-mediated disorder. Disease-associated variants have been found in complement factor H-related genes and complement regulatory genes. Particularly, homozygous deletions of CFHR1, CFHR3, or CFHR4 have been associated with aHUS. Generally, multiplex ligation-dependent probe amplification (MLPA) is used to detect copy number changes in the CFH and CFHR regions. We have developed a cost-effective droplet digital PCR (ddPCR) method for measuring copy number variations (CNVs) in these genes. Methods A total of 41 DNA samples were analyzed using both ddPCR and MLPA to assess large deletions in the CFHR1, CFHR3, CFHR4 regions. The ddPCR primers were designed to avoid rare single nucleotide polymorphisms to enhance hybridization efficiency and accurate identification of deletions within the CFHR regions. The MLPA probe mix (Probemix P236 CFH Region) targeting CFHR regions was purchased from MRC Holland. Results Multiple heterozygous deletions were observed in the CFHR1, CFHR3, and CFHR4 genes. Homozygous deletions of CFHR1 and CFHR3 were seen in 2 cases, while 2 other cases showed homozygous CFHR1 and heterozygous CFHR3 deletions. All 41 samples demonstrated 100% concordance in copy number results between ddPCR and MLPA, as well as consistent reproducibility in both inter- and intratechnologist evaluations. Conclusions Our findings demonstrate that ddPCR is a robust platform to identify large deletions in the CFHR regions. Future studies are needed to evaluate its ability to distinguish full vs partial deletions and to further establish its clinical diagnostic value.

ABSTRACT Objectives Systematic evidence reviews (SERs) produced by the U.S. Agency for Healthcare Research and Quality (AHRQ) Evidence‐based Practice Center (EPC) Program use contextual questions to provide context and background information on the topic. There is currently no standardized approach to address contextual questions in systematic reviews. This study explored the use of publicly available large language models (LLMs) in addressing contextual questions. Study Design Using a set of 20 published and 5 yet to be published SERs, we selected one contextual question per report and used it as a prompt to elicit answers from an LLM (ChatGPT, Bard, Claude, or Perplexity). Two independent reviewers rated the results using a priori established evaluation criteria ( https://osf.io/4k3cu/ ), comparing the response in the SER to LLM‐generated responses. The study was guided by six research questions addressing feasibility, validity of content, validity of structure, mistakes, congruence between responses, and incremental validity of using LLMs to address contextual questions. Results Using minimal prompt engineering produced relevant responses and documented the feasibility of LLM‐generated answers to contextual questions. Responses differed in content and format and are not reproducible (e.g., LLMs update regularly), but LLMs were able to produce articulate, clinically plausible, and well‐structured responses. We detected few factual errors, contradictions, and no instance of suspected bias, but citations supporting LLM‐generated responses could often not be produced or could not be verified (‘confabulations’). Congruence with human generated responses varied, with LLM‐generated responses providing more background on the topic and SERs providing more nuanced answers in response to the contextual question. Results regarding incremental validity were mixed and may depend on the tool. Conclusion LLMs are potentially helpful in addressing contextual questions in systematic reviews but human expertise remains essential for using the generated information in a meaningful way.

Abstract Background The ability of large language models (LLMs) to work collaboratively and screen studies in a systematic review (SR) is under-explored. Hence, we aimed to evaluate the effectiveness of LLMs in automating the process of screening in systematic reviews. Methods This is an observational study which included labeled data (title and abstracts) for five SRs. Originally, two reviewers screened the citations independently for eligibility. A third reviewer cross-checked each citation for quality assurance. GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 were used using zero-shot chain-of-thought prompting. Collaborative approaches included (i): conflict resolution using benefit of the doubt, (ii) majority voting using an independent third LLM and (iii) conflict resolution using an informed third LLM. Performance was assessed using accuracy, precision for exclusion, and recall for inclusion. Work saved over samples (WSS) was computed to estimate the reduction in manual human effort. Results A total of 11300 articles were included in this study. The individual models, GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 exhibited a high precision for exclusion, achieving 99.7%, 99.7%, and 99.2% and high recall for inclusion achieving 95.5%, 96.6% and 85.7%, respectively. However, the collaborative approach utilizing the two best-performing models (GPT-4 and Claude-3S) achieved an average precision of 99.9% and a recall of 98.5% (across all collaborative approaches). Furthermore, the proposed collaborative approach resulted in an average WSS of 63.5%, compared to the average WSS of 45.2% for individual models. Conversational LLM interactions showed a consistent pattern of results. Limitations This study was limited due to reliance on proprietary models, and evaluation on oncology datasets. Conclusion Evidence shows that collaborative LLMs enable efficient, high-performing screening in systematic reviews, supporting continuous evidence updates. Primary funding source NIH (U24CA265879-01-1) and Carolyn-Ann-Kennedy-Bacon Fund.

Objective: The objective of this investigation was to evaluate the agreement rate on judgments made using the Murad tool by different systematic review teams. Introduction: Evaluating the methodological quality of case reports and case series is challenging but some tools do exist for this purpose. We leveraged the presence of studies that have been evaluated by different systematic review teams to assess the inter-consensus agreement among different teams on Murad tool domains. Methods: Using a back-citation method, we identified systematic reviews that used the Murad tool and retrieved all the included primary studies. We selected studies that were assessed by more than 1 systematic review team. We calculated observed agreement and Gwet’s agreement coefficient on judgments made about each signaling question. Results: We identified 982 systematic reviews that cited the Murad tool and collectively cited 59,080 references. The final data comprised of 81 duplicated case reports and series assessed by more than 1 systematic review team. Overall, the signaling questions had very high observed agreement with 5 of the 8 questions having agreement over 75%. The signaling questions with the highest agreement addressed the adequacy of the ascertainment of the exposure (coefficient 0.959), ascertainment of the outcome (coefficient 0.829), presence of a dose-response gradient (perfect agreement), and clarity of reporting (coefficient 0.755). Conclusion: The current study demonstrates overall high agreement among different systematic review teams that used the Murad tool for appraisal of the same case series and case reports. Leveraging duplicated studies across systematic reviews is a feasible way to retrospectively assess the reliability of the tool.