Background: The Swanson Nolan, and Pelham scale version IV (SNAP-IV) is the most critical tool for ADHD screening and diagnosis, which has two scoring methods. ADHD requires symptom assessment in multiple scenarios, and parent and teacher reports are indispensable for diagnosing ADHD. But the differences of assessment results from fathers, mothers and teachers, and the consistency of results from different scoring methods are unknown. Therefore, we carried out this study to understand the differences in the scores of fathers, mothers and teachers using SNAP-IV for children with ADHD and to explore the differences in scoring results under different scoring methods.Methods: The SNAP-IV scale and Demographics Questionnaire and Familiarity Index were used to survey fathers, mothers and head teachers. Measurement data are expressed as the mean ± standard deviation (x ± s). The enumeration data were described by frequency and percentage. ANOVA was used to compare group differences in mothers’, fathers’, and teachers’ mean SNAP-IV scores. The Bonferroni method was used for post hoc multiple comparison tests. Cochran’s Q test was used to compare the differences in the abnormal rate of SNAP-IV score results of mothers, fathers and teachers. Dunn’s test was used for post hoc multiple comparison tests.Results: There were differences in scores among the three groups, and the differences showed inconsistent trends across the different subscales. Differences between groups were calculated again with familiarity as a control variable. The results showed the familiarity of parents and teachers with the patients did not affect the differences in their scores. The evaluation results were different under two assessment methods.Conclusion: Results concluded that fathers did not appear to be an appropriate candidate for evaluation. When using the SNAP-V for assessment, it should be comprehensively considered from both the scorer and symptom dimensions.

Besides the ID class features, the advertisement click log file contains many significant features, which make the study of the advertisement clickthrough rate prediction more difficult. In this study, we convert original features into numerical meaningful ones, which reduce the sparsity and redundancy. In order to solve the problem of class imbalance, we propose a downsampling algorithm based on K-means model to classify large samples, then divide them into some sensible and rational features by the heuristic methods. To further improve the feature representation, we finally select and combine features by the Gradient Boosting Decision Tree model and process high-dimensional features by the logistic regression method. We conducted experiments on the dataset of Tencent SOSO and demonstrated that our approach outperforms the state-of-the-art baseline methods by 0.05% on average in terms of R2 and by 50.5% on average in terms of RMSE.

Rationale: Zolpidem, a non-benzodiazepine sedative-hypnotic, is considered safer for the treatment of insomnia compared to benzodiazepines. However, in recent years, there have been growing reports of Zolpidem dependence, addiction, and withdrawal symptoms. We reported a case of Zolpidem addiction and successful detoxification, reviewed similar cases in the literature, and proposed a potential mechanism underlying Zolpidem addiction. Patient concerns: The patient was a 46-year-old Tibetan woman who had been using Zolpidem intermittently to treat insomnia for at least 8 years. She was overweight, with a BMI of 28.04 kg/m², and had hypertension, diabetes, a 20-year smoking history, and several years of alcohol abuse, often seeking instant gratification. She voluntarily increased both the dosage and frequency of Zolpidem, experiencing euphoria, anxiolysis, and increased appetite at higher doses, which led her to gradually escalate her dosage to 280 mg per day in divided doses. However, upon stopping Zolpidem, she experienced withdrawal symptoms, including insomnia, tension, and palpitations. Diagnoses: She was diagnosed with a combination of hypnotic use disorder, anxiety disorder, hypertension, and diabetes. Interventions: She underwent diazepam replacement therapy, along with antianxiety medications and mindfulness-based cognitive therapy, to address Zolpidem addiction. Outcomes: After 13 days of inpatient treatment, the patient successfully quit Zolpidem. During a 3-month follow-up, she returned to work and remained free from Zolpidem use. Lessons: We speculate that Zolpidem addiction is likely linked to the drug’s euphoric effects and certain patient characteristics, such as sensation-seeking behavior. A comprehensive approach, involving both pharmacological and psychological interventions, is essential for an effective detoxification strategy for Zolpidem addiction.