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Objective Recently, several scientific societies designed ultrasound (US) risk stratification systems (RSS) to guide the workup of thyroid nodules and decide which nodules should undergo fine-needle aspiration cytology (FNAC). However, these systems have been developed against papillary thyroid carcinoma, and scanty data on their role in identifying medullary thyroid carcinoma (MTC) are available. The aims of this study are to describe the US features of MTC and evaluate the performance of RSS in identifying MTC. Methods Data of 152 consecutive patients with MTC was evaluated. The results of the pre-operative neck US of all patients were collected. Ultrasound features of each MTC were evaluated and classified according to the five main RSS available. Results Median MTC dimension was 1.3 cm. Most of the nodules showed solid composition, hypoechoic pattern, and regular margins. About half of them showed the presence of calcifications, but only a subgroup had microcalcifications. A minority of the nodules showed a 'taller than wide' shape. Only 7.9% of all MTC showed the simultaneous presence of at least four US features suggestive of malignancy. Ultrasonographic high-risk of malignancy of the MTC included in the five RSS, varied from 45.4 to 47.4%, and performing FNAC was suggested in only 48.7 to 63.8% of all MTC. Conclusions In this series, neither single nor the association of US features are specific for MTC. The five main RSS correctly identify less than 50% of MTC and do not suggest performing FNAC in about half of them with potentially missed or delayed diagnosis.

Background: Long COVID has become a burden on healthcare systems worldwide. Research into the etiology and risk factors has been impeded by observing all diverse manifestations as part of a single entity. We aimed to determine patterns of symptoms in convalescing COVID-19 patients. Methods: Symptomatic patients were recruited from four countries. Data were collected regarding demographics, comorbidities, acute disease and persistent symptoms. Factor analysis was performed to elucidate symptom patterns. Associations of the patterns with patients’ characteristics, features of acute disease and effect on daily life were sought. Results: We included 1027 symptomatic post-COVID individuals in the analysis. The majority of participants were graded as having a non-severe acute COVID-19 (N = 763, 74.3%). We identified six patterns of symptoms: cognitive, pain-syndrome, pulmonary, cardiac, anosmia-dysgeusia and headache. The cognitive pattern was the major symptoms pattern, explaining 26.2% of the variance; the other patterns each explained 6.5–9.5% of the variance. The cognitive pattern was higher in patients who were outpatients during the acute disease. The pain-syndrome pattern was associated with acute disease severity, higher in women and increased with age. The pulmonary pattern was associated with prior lung disease and severe acute disease. Only two of the patterns (cognitive and cardiac) were associated with failure to return to pre-COVID occupational and physical activity status. Conclusion: Long COVID diverse symptoms can be grouped into six unique patterns. Using these patterns in future research may improve our understanding of pathophysiology and risk factors of persistent COVID, provide homogenous terminology for clinical research, and direct therapeutic interventions.