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Objective Ability to thrive after invasive and intensive treatment is an important parameter to assess in patients with glioblastoma multiforme (GBM). Karnofsky Performance Status (KPS) is used to identify those patients suitable for postoperative radiochemotherapy. The aim of the present study is to investigate whether machine learning (ML)-based models can reliably predict patients' KPS 6 months after surgery. Methods A cohort of 416 patients undergoing surgery for a histopathologically confirmed GBM were collected from a multicentric database and split into a training and hold-out test set in an 80:20 ratio. Worsening of KPS at 6 months after surgery (compared with preoperative KPS) occurred in 138 patients (33.2%). Relevant preoperative, intraoperative, and immediately postoperative variables were selected by a recursive features selection algorithm (Boruta) and used to build 2 ML-based predictive models. Results A random forest classifier and a random forest regressor were trained to predict 6 months postoperative KPS as a categorical (worsening vs. stable/improving) and continuous variables; they achieved, respectively, an area under the curve of 0.81 (95% confidence interval, 0.76–0.84) and a mean absolute error of 4.4 (95% confidence interval, 4.0–4.7). Leveraging the predictive value resulting from the combination of independent variables, the random forest classifier outperformed conventional statistics (area under the curve improvement of +21%). Conclusions Two robust ML-based prediction models were successfully trained and internally validated. Considerable effort remains to improve the interpretation of the results when these predictions are used in a patient-centered care context.

Background Despite the rising trend of medicolegal challenges in Italy, there is a significant lack of literature on this topic. To provide better understanding of awareness toward medicolegal aspects, defensive behavior, consent-taking practice, and general perceptions of Italian neurosurgeons on this issue, a cross-sectional survey was conducted. Methods A questionnaire covering various aspects of medicolegal issues, in an anonymous online form, was sent to neurosurgeons practicing in Italy via e-mail—through a national mailing list—and social media platforms. The data collection period was November 1, 2020 to December 31, 2020. Results A total of 64 single responses were included. We assessed 1) awareness of Italian neurosurgeons about a legal framework: a low level of knowledge has been reported—87.5% were unaware of the terms and conditions outlined by the contract with the institution, 75.6% of respondents stated they didn't know the main body of law that governed the medical community; and 2) perception toward defensive medicine: 92.2% believed that they were not trained to face possible medicolegal issues, but only a few tried to acquire more knowledge about medical legal issues, such as learning how to obtain a proper informed consent for a surgical procedure (34.4%). Conclusions This study marks the first survey to identify the perceptions among Italian neurosurgeons of medical legal issues and related “defensive medicine” practices: This is of relevance given its exorbitant cost in terms of public expenditure, psychologic burden, and its relevant impact on health care delivery.