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Abstract Objectives Reporting the clinicopathological information of thyroid cancer (TC) patients from a central medical center of east China, and constructing the nomogram predicting lymph node metastasis (LNM). Methods We collected the patients who underwent thyroid cancer surgery in our institute from July 1, 2019 to July 31, 2021, a total of 253 subjects were enrolled. We used HiPure FFPE DNA Kit to extract DNA and RNApure FFPE Kit to extract RNA from the paraffin sections of tumor tissue, the extracted DNA samples and RNA samples were used for NGS sequencing. The clinical and pathological information of TCGA‐THCA cohort was obtained as the validation cohort. Multivariate logistic regression analysis was performed to identify the independent prognostic factor, and the nomogram was subsequently constructed by “rms” R package. Results Secondary cases contained more mutation of BRAF (90.48% vs. 62.07%) and TERT (33.0% vs. 3.0%), as compared with primary cases. Primary patients with positive lymph node were younger (40.9 ± 10.8 vs. 45.3 ± 11.8, p = 0.0031) and contained advanced TI‐RADS levels (4c: 22.8% vs. 8.3%, 5: 6.5% vs. 0/0%, p = 1.878e‐03), as well as more RET genetic alteration (16.3% vs. 2.7%, p = 2.566e‐03). We chose age, tumor diameter, RET fusion, and gender to construct the LNM predicting nomogram. Calibration plot, DCA curve, and the clinical impact plot verified the preferable prognostic value of the nomogram, with an AUC value of 0.724 (0.656–0.792). We successfully validated the prognostic value of the nomogram in TCGA‐THCA cohort. RET fusion might impact the process of protein digestion and absorption, cytokine‐cytokine receptor interaction, ECM‐receptor interaction, focal adhesion. Conclusion We provide a novel nomogram to predict the LNM for TC patients, including the features of patient's age, gender, tumor diameter, and RET alteration. Further studies from multiple medical centers are essential to validate the nomogram.

With the continuous development of education, personalized learning has attracted great attention. How to evaluate students' learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student's learning outcomes, such as "scores" or "right/wrong," which seldom reflects the development of students' cognitive level and lacks effective diagnostic information. This article proposes a non-parametric multi-level scoring cognitive diagnosis method based on the KNN and the characteristics of information technology courses named the EW-KNN (E-weight K-Nearest Neighbor). Compared with the KNN, the EW-KNN improved two key points. One is that it takes the number of IRP (Ideal Response Pattern) as the K value to adapt to different types of tests. The other is that the nearest neighbor distance is introduced to solve the problem of misjudgment of the categories. The Monte Carlo simulation method is used to test its performance. The results indicate that the EW-KNN has a higher accuracy rate and is suitable for information technology courses. Furthermore, the method is applied in information technology course to make a cognitive diagnosis of 120 students of high school in Shanghai. Results demonstrate that the EW-KNN can accurately diagnose each student's cognition levels and knowledge structure accurately.