OBJECTIVE: Acute appendicitis is the most common cause of acute abdomen. It is important to differentiate between complicated and uncomplicated appendicitis before surgery in the emergency department. Recently, immature granulocytes have become one of the biomarkers used as predictors of inflammation. The aim of this study was to determine whether immature granulocyte levels are a biomarker that can predict whether acute appendicitis is complicated or not in patients admitted to the emergency department.

Background: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. Methods: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann–Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. Results: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. Conclusion: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

Background/Objectives: Bone age assessment is critical in pediatric endocrinology and forensic medicine. Although recently developed multimodal large language models (LLMs) show potential in medical imaging, their diagnostic performance in bone age determination has not been sufficiently evaluated. This study evaluates the performance of four multimodal LLMs (ChatGPT-5, Gemini 2.5 Pro, Grok-3, and Claude 4 Sonnet) in bone age determination using the Gilsanz–Ratib (GR) atlas. Methods: This retrospective study included 245 pediatric patients (109 male, 136 female) under the age of 18 who underwent left wrist radiography. Each model estimated bone age using the patient’s radiograph and GR atlas as reference (atlas-assisted prompting). Bone age assessments made by an experienced radiologist using the GR atlas were evaluated as the reference standard. Performance was assessed using mean absolute error (MAE), intraclass correlation coefficient (ICC), and Bland–Altman analysis. Results: ChatGPT-5 demonstrated statistically superior performance, with an MAE of 1.46 years and ICC of 0.849, showing the highest alignment with the reference standard. Gemini 2.5 Pro showed moderate performance, with an MAE of 2.24 years; Grok-3 (MAE: 3.14 years) and Claude 4 Sonnet (MAE: 4.29 years) had error rates that were too high for clinical use. Conclusions: Significant performance differences exist among multimodal LLMs, despite atlas-supported prompting. Only ChatGPT-5 qualified as “clinically useful,” demonstrating potential as an auxiliary tool or educational support under expert supervision. Other models’ reliability remains insufficient.

Currently, unilateral clinical and subclinical axillary adenopathy cases associated with the Pfizer-BioNTech and Moderna vaccines are increasingly reported. However, only one study on axillary adenopathy due to the CoronaVac vaccine is published. To present the incidence, severity, and ultrasonographic findings of axillary adenopathy that developed in healthcare professionals in Turkey after they were vaccinated with CoronaVac against coronavirus disease-19. A prospective study. In Turkey, the first dose of the CoronaVac vaccine for coronavirus disease-19 was administered to healthcare professionals on January 14, 2021, and the second dose on February 11, 2021. This study covered the period from January 21, 2021 (1 week after the first dose), and April 15, 2021 (9 weeks after the second dose). Individuals who had a history of COVID-19 more than 3 weeks after vaccine doses, systemic disease, and diagnosis and treatment history of breast cancer were excluded. The axillary lymph nodes of the vaccinated and contralateral arms were evaluated in 101 volunteer healthcare professionals using axillary ultrasonography. A significant difference was found in the cortical thicknesses of the lymph nodes between the vaccinated and contralateral axilla after both the first (*p < 0.01) and second (*p < 0.01) doses. Accordingly, the rates of subclinical lymphatic hyperplasia on the vaccinated side were 25.7% (n = 26/101) after the first and 31.1% (n = 28/90) after the second dose. Lymph nodes with pathological appearance based on a reduced echogenic hilum with marked cortical thickening were found only in 2.2%. Among the 39 cases in which antibodies (immunoglobulin G and immunoglobulin M) were measured, the antibody level was classified as <10 and ≥10. No statistically significant difference was found in the cortical thickness of the axillary lymph nodes between patients with high antibody levels (≥10) and those with low antibody levels (<10) (p > 0.05). In this study, clinical signs of axillary lymph node hyperplasia were not detected after vaccination with CoronaVac. Mild and diffuse thickening of the CoronaVac vaccine-induced lymph nodes was more common than pathological and palpable lymph nodes.

Attenuation values in computed tomography (CT) are used as a diagnostic aid in certain clinical conditions. In our study, we investigated the effectiveness of attenuation values, obtained through the noninvasive method of CT, in determining the type of pleural effusions in the emergency department. Patients who presented to the emergency department with a diagnosis of pleural effusion and underwent thoracentesis within 48 hours between January 1, 2023, and January 1, 2024, were included in the study. Exclusion criteria were patients under 18 years of age, those without CT imaging, those presenting due to trauma, those with >48 hours between CT and thoracentesis, patients with chest tubes, a history of thoracic surgery/intervention within the last month, or incomplete data. Attenuation values measured on CT were compared with pleural fluid samples interpreted according to Light criteria. A total of 207 patients were evaluated. The mean age in the exudate group (66.00 ± 15.63) was significantly lower than in the transudate group (72.98 ± 10.38) (P < .001). The prevalence of malignancy was significantly higher in the exudate group (34.0%) compared to the transudate group (4.4%) (P < .001). Heart failure was more common in the transudate group (31.1%) (P = .041). The mean Hounsfield unit (HU) value was 16.64 ± 8.04 in the exudate group and 12.22 ± 7.01 in the transudate group, with a statistically significant difference between the groups (P = .001, 95% confidence interval [CI]: 1.79–7.05). At the cutoff point of 14.2, sensitivity was 62.73%, specificity was 75.00%, and the Youden index reached its highest value at 0.377. The area under the curve was 0.710 (95% CI: 0.626–0.794, P < .001), and the positive predictive value at the 14.2 cutoff point was 90.18%. In conclusion, our study demonstrated that the use of CT attenuation in managing pleural effusions in the emergency department can accurately detect exudative effusions as a noninvasive method. However, HU values should be evaluated alongside clinical parameters, and the varying cutoff values reported in different studies suggest that this method may not be sufficient on its own. In the emergency department setting, the use of the HU scale could improve patient management by avoiding complications associated with invasive procedures like diagnostic thoracentesis.