Patients exposed to a surgical safety checklist experience better postoperative outcomes, but this could simply reflect wider quality of care in hospitals where checklist use is routine.

Background: Ultrasound (US) examination is helpful in the differential diagnosis of thyroid nodules (malignant vs. benign), but its accuracy relies heavily on examiner experience. Therefore, the aim of this study was to develop a less subjective diagnostic model aided by machine learning. Methods: A total of 2064 thyroid nodules (2032 patients, 695 male; M age = 45.25-13.49 years) met all of the following inclusion criteria: (i) hemi-or total thyroidectomy, (ii) maximum nodule diameter 2.5 cm, (iii) examination by conventional US and real-time elastography within one month before surgery, and (iv) no previous thyroid surgery or percutaneous thermotherapy. Models were developed using 60% of randomly selected samples based on nine commonly used algorithms, and validated using the remaining 40% of cases. All models function with a validation data set that has a pretest probability of malignancy of 10%. The models were refined with machine learning that consisted of 1000 repetitions of derivatization and validation, and compared to diagnosis by an experienced radiologist. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. Results: A random forest algorithm led to the best diagnostic model, which performed better than radiologist diagnosis based on conventional US only (AUC = 0.924 [confidence interval (CI) 0.895-0.953] vs. 0.834 [CI 0.815-0.853]) and based on both conventional US and real-time elastography (AUC = 0.938 [CI 0.914-0.961] vs. 0.843 [CI 0.829-0.857]). Conclusions: Machine-learning algorithms based on US examinations, particularly the random forest classifier, may diagnose malignant thyroid nodules better than radiologists.

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BackgroundSkip metastasis is a special type in cervical lymph node metastasis (LNM) of patients diagnosed with papillary thyroid carcinoma (PTC) which induced poor prognosis. There are few studies about skip metastasis and conclusions remained uncertain. Therefore, this study aims to explore the frequency and to investigate risk factors of skip metastasis in PTC.MethodsThrough searching the keyword by PubMed and Embase databases which articles published up to 1st August 2018 about skip metastasis in papillary thyroid carcinoma, we extract data in order to assure whether those materials meet the criteria.ResultsThe prevalence of skip metastasis is 12.02% in light of our meta-analysis of 18 studies with 2165 patients. The upper pole location (RR = 3.35, 95% CI =1.65–6.79, P = 0.0008) and tumors size ≤1 cm (RR = 2.65, 95% CI =1.50–4.70, P = 0.0008) are significantly associated with skip metastasis, whereas lymphovascular invasion (RR = 0.33, 95% CI =0.15–0.75, P = 0.0083) exists lower rate of skip metastasis. Multifocality, gender, age, bilaterality, thyroiditis and Extrathyroidal extension (ETE) are insignificantly associated with skip metastasis. Level II and level III are the most frequently affected areas.ConclusionThe lateral compartment should be carefully examined especially for those PTC patients who present primary tumors in the upper lobe with a primary tumor size ≤10 mm which could be detected with skip metastasis.