Faculty Bibliography

RATIONALE AND OBJECTIVES/OBJECTIVE:To evaluate the performance, stability, and decision-making behavior of large language models (LLMs) for title and abstract screening for radiology systematic reviews, with attention to prompt framing, confidence calibration, and model robustness under disagreement. MATERIALS AND METHODS/METHODS:We compared five LLMs (GPT-4o, GPT-4o mini, Gemini 1.5 Pro, Gemini 2.0 Flash, Llama 3.3 70B) on two imaging-focused systematic reviews (n = 5438 and n = 267 abstracts) using binary and ternary classification tasks, confidence scoring, and reclassification of true and synthetic disagreements. Disagreements were framed as either "LLM vs human" or "human vs human." We also piloted autonomous PubMed retrieval using OpenAI and Gemini Deep Research tools. RESULTS:LLMs achieved high specificity and variable sensitivity across reviews and tasks, with F1 scores ranging from 0.389 to 0.854. Ternary classification showed low abstention rates (<5%) and modest sensitivity gains. Confidence scores were significantly higher for correct predictions. In disagreement tasks, models more often selected the human label when disagreements were framed as "LLM vs human," consistent with authority bias. GPT-4o showed greater resistance to this effect, while others were more prone to defer to perceived human input. In the autonomous search task, OpenAI achieved moderate recall and high precision; Gemini's recall was poor but precision remained high. CONCLUSION/CONCLUSIONS:LLMs hold promise for systematic review screening tasks but require careful prompt design and circumspect human-in-the-loop oversight to ensure robust performance.

BACKGROUND:Mild traumatic brain injury is theorized to cause widespread functional changes to the brain. Resting-state fMRI may be able to measure functional connectivity changes after traumatic brain injury, but resting-state fMRI studies are heterogeneous, using numerous techniques to study ROIs across various resting-state networks. PURPOSE/OBJECTIVE:We systematically reviewed the literature to ascertain whether adult patients who have experienced mild traumatic brain injury show consistent functional connectivity changes on resting-state -fMRI, compared with healthy patients. DATA SOURCES/METHODS:We used 5 databases (PubMed, EMBASE, Cochrane Central, Scopus, Web of Science). STUDY SELECTION/METHODS:Five databases (PubMed, EMBASE, Cochrane Central, Scopus, and Web of Science) were searched for research published since 2010. Search strategies used keywords of "functional MR imaging" and "mild traumatic brain injury" as well as related terms. All results were screened at the abstract and title levels by 4 reviewers according to predefined inclusion and exclusion criteria. For full-text inclusion, each study was evaluated independently by 2 reviewers, with discordant screening settled by consensus. DATA ANALYSIS/METHODS:Data regarding article characteristics, cohort demographics, fMRI scan parameters, data analysis processing software, atlas used, data characteristics, and statistical analysis information were extracted. DATA SYNTHESIS/RESULTS:Across 66 studies, 80 areas were analyzed 239 times for at least 1 time point, most commonly using independent component analysis. The most analyzed areas and networks were the whole brain, the default mode network, and the salience network. Reported functional connectivity changes varied, though there may be a slight trend toward decreased whole-brain functional connectivity within 1 month of traumatic brain injury and there may be differences based on the time since injury. LIMITATIONS/CONCLUSIONS:Studies of military, sports-related traumatic brain injury, and pediatric patients were excluded. Due to the high number of relevant studies and data heterogeneity, we could not be as granular in the analysis as we would have liked. CONCLUSIONS:Reported functional connectivity changes varied, even within the same region and network, at least partially reflecting differences in technical parameters, preprocessing software, and analysis methods as well as probable differences in individual injury. There is a need for novel rs-fMRI techniques that better capture subject-specific functional connectivity changes.

This retrospective, single-center study aimed to characterize and compare the kinetics of B-cell reemergence following anti-CD20 infusion (anti-CD20i) in African American (AA) and white patients with MS or NMOSD. In a logistic regression model that included race, time since anti-CD20i, body mass index, and diagnosis, only AA race (p=0.01) and time since anti-CD20i (p=0.0003) were significant predictors of B-cell repletion. However, B-cell subset composition was similar between AA and white patients with detectable CD19+ B-cell counts. These findings highlight the importance of including a diverse study population in future studies of anti-CD20 therapies.