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 The Clavien–Dindo classification is perhaps the most widely used approach for reporting postoperative complications in clinical trials. This system classifies complication severity by the treatment provided. However, it is unclear whether the Clavien–Dindo system can be used internationally in studies across differing healthcare systems in high‐ (HICs) and low‐ and middle‐income countries (LMICs). Methods This was a secondary analysis of the International Surgical Outcomes Study (ISOS), a prospective observational cohort study of elective surgery in adults. Data collection occurred over a 7‐day period. Severity of complications was graded using Clavien–Dindo and the simpler ISOS grading (mild, moderate or severe, based on guided investigator judgement). Severity grading was compared using the intraclass correlation coefficient (ICC). Data are presented as frequencies and ICC values (with 95 per cent c.i.). The analysis was stratified by income status of the country, comparing HICs with LMICs. Results A total of 44 814 patients were recruited from 474 hospitals in 27 countries (19 HICs and 8 LMICs). Some 7508 patients (16·8 per cent) experienced at least one postoperative complication, equivalent to 11 664 complications in total. Using the ISOS classification, 5504 of 11 664 complications (47·2 per cent) were graded as mild, 4244 (36·4 per cent) as moderate and 1916 (16·4 per cent) as severe. Using Clavien–Dindo, 6781 of 11 664 complications (58·1 per cent) were graded as I or II, 1740 (14·9 per cent) as III, 2408 (20·6 per cent) as IV and 735 (6·3 per cent) as V. Agreement between classification systems was poor overall (ICC 0·41, 95 per cent c.i. 0·20 to 0·55), and in LMICs (ICC 0·23, 0·05 to 0·38) and HICs (ICC 0·46, 0·25 to 0·59). Conclusion Caution is recommended when using a treatment approach to grade complications in global surgery studies, as this may introduce bias unintentionally.

Because the abnormal location of the power grid load is mismarked, the average relative error increases in the process of forecasting. This paper proposes to study and analyze the method of short-term load forecasting based on data mining. It preprocesses the load characteristic clustering and related data, marks the abnormal position of a power grid within a calibrated mining range according to the load fluctuation state, and defines the short-term load forecasting range. It also constructs a data mining power load forecasting model through the acquired real-time forecasting data and information, designs layered and staged load forecasting links, and corrects and calculates by adopting data mining. The prediction residual error is obtained. The stage prediction standard is further determined, and the prediction processing is realized. The final test results show that the data mining short-term load forecasting test group designed in this paper has a small fluctuation range, indicating that the prediction error has been greatly controlled in the practical application process. The prediction error is small, the accuracy is improved, and it has practical application value.

To improve the accuracy of power load forecasting, this paper analyzes the defects as well as merits of artificial neural network (ANN) and grey prediction method, and it combines the two methods to propose a novel forecasting method called grey neural network (GNN). GNN utilizes the accumulation generation operation (AGO) of grey prediction to transform the original load data to first order AGO data which has better regularity, making it easier for ANN to model and forecast. At the same time the theoretical error of traditional grey prediction method is avoided. GNN is suitable for middle and long term load forecasting, and case study shows that its forecasting accuracy is better than that of ANN and grey prediction method. The paper also reveals the importance of data transformation in load forecasting process, and it further investigates the effect of inverse transformation on forecasting error.