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AbstractBackgroundThe clinical significance of natural and treatment‐emergent antibodies specific for amustaline/glutathione pathogen‐reduced red blood cells (PRRBCs) is not known.Study Design and MethodsA Phase 3, randomized clinical trial of PRRBCs (ReCePI) compared PRRBCs with conventional RBCs in cardiac or thoracic‐aorta surgery. Subjects transfused during and for 7 days after surgery were screened for PRRBC‐specific antibodies at baseline, 28 and 75 days post‐surgery. Subjects with treatment‐emergent antibodies were assessed for evidence of hemolysis. Cryopreserved subject RBC samples were assayed by flow cytometry for circulating PRRBCs using an acridine‐specific (2S197‐2M1) monoclonal antibody, and for human IgG‐coated RBCs. RBC‐surface acridine density was quantitated using a commercial calibrated phycoerythrin (PE)‐bead panel.ResultsFive of 159 (3.1%) PRRBC and zero of 162 conventional RBC recipients developed treatment‐emergent PRRBC‐specific IgG, low titer antibodies detected 26–80 days post‐surgery after exposure to 1–3 PRRBC units, without clinical evidence of hemolysis. DAT and eluate were weak (w+) positive and PRRBC‐specific in one subject. A monocyte monolayer assay (MMA) was non‐reactive in the three subjects with an interpretable result. Flow cytometry demonstrated circulating PRRBCs in all five subjects expressing surface acridine concentrations at the limit of detection (approximately 150–301 PE molecules/RBC) compared with freshly transfused PRRBCs (approximately 7500 PE molecules/RBC). In some samples, loss of surface acridine expression could not be distinguished from clearance of the PRRBCs.DiscussionTreatment‐emergent PRRBC‐specific antibodies with the characteristics of nonclinically significant antibodies were detected in five subjects transfused with PRRBCs. Flow cytometry demonstrated persistent circulating PRRBCs with minimal surface acridine expression. (www.ClinicalTrials.gov Identifier NCT03459287).

Objective: To retrospectively analyze publicly available elbow ulnar collateral ligament reconstruction (UCLR) injury data for professional baseball players. Design: Descriptive epidemiology study. Setting: A retrospective analysis using an open-source database was performed. The database contained all known UCLR surgeries among Major League Baseball (MLB) and Minor League Baseball (MiLB) players. Participants: In total, 1801 professional male professional baseball pitchers (age: 24.5 ± 3.9 years) who have undergone UCLR between 1974 and 2024. Main Outcome Measures: The number of UCLR procedures each year. Independent Variables: Change-point analysis was used to identify changes in the number of UCLR surgeries over time among MLB and MiLB players. For MLB players, number of UCLR surgeries from 1974 to 2023 was analyzed, while for MiLB players, data starting from 1981 to 2024 were analyzed. Results: For MLB players, 3 change points were identified, occurring around 1989 (95% confidence interval [CI], 1988–1990), 2000 (95% CI, 2000–2000), and 2012 (95% CI, 2011–2017). For MiLB players, 3 change points were identified occurring around 2001 (95% CI, 2001–2001), 2009 (95% CI, 2008–2010), and 2013 (95% CI, 2013–2015). Conclusions: Overall, there has been a rise in the number of UCLR surgeries per year for the past 50 years in professional baseball, with distinct time points identified, representing increases in the number of UCLR surgeries each year. A higher number of UCLR surgeries are performed per year in the MiLB than in the MLB.

Abstract Endocrinopathies are frequently the initial presentation of histiocytic neoplasms, which are rare hematologic disorders affecting multiple organ systems. Langerhans cell histiocytosis and Erdheim–Chester disease are 2 such disorders known to infiltrate the hypothalamus and/or pituitary gland, leading to arginine vasopressin deficiency (AVP-D) and anterior pituitary dysfunction (APD) in 20% to 30% of cases, often as the first manifestation. Conversely, histiocytic disorders account for a notable proportion (10-15%) of all pituitary stalk lesions. The diagnosis of histiocytoses is often delayed in such cases due to the nonspecific presentation of endocrinopathies and pituitary involvement. Consequently, endocrinologists are at the frontline and uniquely positioned to achieve early diagnosis by recognizing the varied nonendocrine features of these disorders. This article provides an overview of the endocrine manifestations of histiocytic disorders and presents a simplified algorithm to guide the diagnostic workup in cases presenting with “idiopathic” AVP-D or APD. Such cases should be evaluated for histiocytic neoplasms with additional imaging studies and biopsies of suspected disease sites. If no disease site beyond the pituitary is identified, the risks and benefits of a pituitary stalk lesion biopsy must be carefully considered. While treatments of histiocytic neoplasms are highly efficacious, endocrinopathies are considered permanent and require long-term hormone replacement. It remains unclear whether early diagnosis and novel targeted therapies can reverse these endocrine disorders. Therefore, the role of the endocrinologist role is critical in the diagnosis and management of these rare diseases.

Abstract Deep learning is revolutionizing medical imaging analysis by enabling the classification of various pathoanatomical conditions at scale. Unfortunately, there have been a limited number of accurate and efficient machine learning (ML) algorithms that have been developed for the diagnostic workup of morphological hip pathologies, including developmental dysplasia of the hip and femoroacetabular impingement. The current study reports on the performance of a novel ML model with YOLOv5 and ConvNeXt-Tiny architecture in predicting the morphological features of these conditions, including cam deformity, ischial spine sign, dysplastic appearance, and other abnormalities. The model achieved 78.0% accuracy for detecting cam deformity, 87.2% for ischial spine sign, 76.6% for dysplasia, and 71.6% for all abnormalities combined. The model achieved an Area under the Receiver Operating Curve of 0.89 for ischial spine sign, 0.80 for cam deformity, 0.80 for dysplasia, and 0.81 for all abnormalities combined. Inter-rater agreement among surgeons, assessed using Gwet’s AC1, was substantial for dysplasia (0.83) and all abnormalities (0.88), and moderate for ischial spine sign (0.75) and cam deformity (0.61).

The Quantitative Imaging Biomarkers Alliance Profile for dynamic susceptibility contrast (DSC) MRI and its longitudinal claims for the precision of a DSC MRI–derived quantitative imaging biomarker to assess the relative cerebral blood volume changes in gliomas are summarized.

Abstract Aims The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored. Methods and results Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning and Quantized Low-Rank Adaptation fine-tuning (FT) for echo report summarization from ‘Findings’ to ‘Impressions.’ Against cardiologist-generated Impressions, the models’ performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists. The development dataset included 97 506 reports from 71 717 unique patients, predominantly male (55.4%), with an average age of 64.3 ± 15.8 years. EchoGPT, a fine-tuned Llama-2 model, outperformed other models with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness (P < 0.001), with no significant preference in completeness, correctness, and clinical utility). Correlations between automatic and human metrics were fair to modest, with the best being RadGraph F1 scores vs. clinical utility (r = 0.42) and automatic metrics showed insensitivity (0–5% drop) to changes in measurement numbers. Conclusion EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.

An uncertainty-aware deep learning model developed using Mondrian conformal prediction demonstrated high performance in intracranial hemorrhage detection on noncontrast head CT volumes and high accuracy in identifying challenging cases.

Background: Osteochondritis dissecans (OCD) of the knee is a focal idiopathic alteration of subchondral bone and/or its precursor with risk for instability and disruption of adjacent cartilage. Treatment options focused on preventing premature osteoarthritis vary depending on multiple patient and lesion characteristics, including lesion mobility. Purpose: To differentiate lesion mobility before arthroscopy using a multivariable model that includes patient demographic characteristics and physical examination findings. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: Demographic, preoperative physical examination, and radiographic data were collected from a multicenter national prospective cohort of patients with OCD of the knee. Inclusion criteria included patients <19 years of age and patients with arthroscopically confirmed mobility status based on the Research on Osteochondritis Dissecans of the Knee arthroscopy classification. Multivariable logistic regression analysis using stepwise model selection was used to determine factors associated with the likelihood of a mobile versus an immobile lesion. A 75% partition of the data was used for model training, and 25% was used as a validation cohort. Quantitative model fit statistics were computed using the holdout data, including sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), along with the corresponding 95% CI. Results: A total of 407 patients in the prospective cohort met inclusion criteria, and 62% were male. The mean ± SD age was 13.7 ± 2.2 years, height 161.8 ± 5.3 cm, and weight 59.2 ± 42.2 kg. Arthroscopic evaluation yielded 235 immobile and 172 mobile lesions. Multivariable analysis determined that the best model to predict lesion mobility included chronologic age ≥14 years ( P < .001), effusion on physical examination ( P < .001), and any loss of range of motion on physical examination ( P = .07), while controlling for male sex ( P = .38) and weight >54.4 kg ( P = .12). In the 25% holdout validation sample (n = 102), a sensitivity of 83%, a specificity of 82%, and an AUC of 0.89 (95% CI, 0.82-0.95) were achieved with these predictive factors. Conclusion: Age, effusion, and loss of motion can predict knee OCD lesion mobility at the time of arthroscopy. Education about lesion mobility can help with surgical planning and patient and family counseling.