The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

A Shareholder\u27s Derivative Action\u27s Conditional Use and Possibility of Abuse : A Comparison of Chinese and Japanese Company Law

はじめに　第一章　中国における株主代表訴訟制度の導入の意義及び概要　第一節　中国における株主代表訴訟制度の導入の意義　一　これまでの中国証券市場の状況　二　株主代表訴訟制度導入前の株主訴訟の状況　第二節　中国における株主代表訴訟制度の概要　一　制度の紹介　中国新会社法上の規定について　二　中・日における制度の比較　第三節　小括　第二章　中国会社法における株主代表訴訟制度の機能性　第一節　中国における株主代表訴訟の判例の紹介　第二節　制度機能への影響(一)　手数料の問題　一　裁判実務の問題点の検討及び学説の反応　二　日本法との比較　非財産訴訟とすることの妥当性の検討　第三節　制度機能への影響(二)　原告適格の問題　第四節　制度機能への影響(三)　弁護士費用の負担の問題　一　中国の場合　二　日本の場合　第五節　制度機能への影響(四)　裁判所の対応　一　中国における最高裁判所の｢司法解釈｣の位置づけ　二　会社法関係の司法解釈　三　最高裁判所の株主代表訴訟制度に対するいくつかの司法解釈　第六節　小括　第三章　担保提供制度　第一節　中国における株主代表訴訟制度の濫用の可能性　第二節　濫用防止に関する対策　一　持株期間の制限　二　訴訟委員会　三　裁判所による却下　四　担保提供命令　第三節　日本における担保提供制度　一　担保提供制度の構造及び学界の議論　二　担保提供命令の要件たる｢悪意｣　について　第四節　中国における担保提供制度運用の検討　一　担保提供制度の必要性に関する議論　二　担保提供制度の必要性　三　担保提供命令の要件たる｢悪意｣　について　第五節　小括　結びに代え

Fuzzy Logic and Correlation-Based Hybrid Classification on Hepatitis Disease Data Set

International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYDevelopments in the health field are closely affecting humanity. The development of information technologies increases this effect. In this study, it was aimed to help the decision makers by increasing the accuracy rate in the detection of hepatitis disease. The data set was obtained from UCI machine learning source. Data preprocessing, attribute selection and classifier models were established on this data set, respectively. After the deficiency in the data of the patients with hepatitis was normalized, correlation-based and fuzzy-based rough force attribute selection methods were applied and the attributes that contributed to the classification were selected. The hepatitis dataset and the data set formed by the attributes determined by the correlation-based and the fuzzy-based rough-attribute selection methods were classified using the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression algorithms and the results were compared. Accuracy, sensitivity precision, ROC curve and F-measure values were used in the comparison of classification algorithms. In the process of separating the data set as a test and training set, a 5-fold cross-validation method was applied. It has been observed that the fuzzy rough clustering algorithm is more successful than the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression classification methods in the detection of hepatitis disease.WOS:0006787710000682-s2.0-8508345020