Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Interdisciplinary cardiovascular and thoracic surgery
Evaluating Alleged Malpractice Cases Against Cardiologists and Cardiothoracic Surgeons in The Netherlands
This study aimed to analyse alleged malpractice cases and outcomes against cardiologists and cardiothoracic surgeons in the Netherlands. The Dutch Medical Disciplinary Database was searched for verdicts of alleged malpractice cases published between January 2010 and October 2025. Data were collected on case details and outcomes, and descriptive statistics were employed. A total of 219 cases were included (182 cases for cardiology and 37 cases for cardiothoracic surgery). Most cases were due to incorrect treatment/diagnosis for cardiologists and cardiothoracic surgeons (57% and 68%, respectively). Against cardiologists, 45/182 cases (25%) were judged as (partially) founded, for which 25 warnings, 17 reprimands, 1 temporary suspension, and 1 full practice prohibition were imposed. Against cardiothoracic surgeons, 11/37 cases (30%) were judged as (partially) founded, for which 10 warnings were imposed. We report trends and outcomes of alleged malpractice claims against cardiologists and cardiothoracic surgeons in the Netherlands. The cases discussed in this article offer valuable insights that can help enhance patient care.
11 June 2026
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Risk assessment of civil aviation cabin safety incidents based on the CNN-LSTM-Attention model
Against the backdrop of the thriving global civil aviation industry, cabin safety, which is a crucial link in civil aviation safety systems, has prompted an increasingly urgent need for intelligent risk management. This study constructs an integrated framework of "deep text mining-intelligent risk assessment" and proposes a hybrid CNN-LSTM-Attention model for cabin safety incident classification. Using 8,280 global cabin abnormal event reports from 2004-2024, the model achieves 95.01% accuracy and an F1 score of 94.17% on the test set, substantially outperforming benchmark approaches. Compared with XGBoost (89.45% accuracy, 88.98% F1), the proposed model improves accuracy by 5.56% and the F1 score by 5.19%, demonstrating superior capability in extracting deep semantic features and identifying key risk patterns. The framework also establishes a "text feature-risk mechanism-hazard level" mapping system, enabling more interpretable risk quantification. The findings provide an effective technical path for intelligent early warning of cabin safety risks and offer methodological support for data-driven decision-making in civil aviation safety management.
30 May 2026
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