Objective. To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach. Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50–0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P < 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

Objective: To analyze the context and use of the Nursing Delirium Screening Scale (Nu-DESC) for early detection of delirium in adult patients, compiling the available evidence. Method: Searching for relevant articles on databases such as Cinahl, Medline, Ovid, Scopus, and Web of Science. Inclusion criteria: Articles written in English, Spanish, and Portuguese, published between January 2013 and October 2019. Search terms: “nursing delirium screen,” “inpatient delirium screening,” and “nursing assessment.” We identified 23 articles in which the Nu-DESC was used. Two reviewers independently assessed the articles using the CASPe (Critical Appraisal Skills Program in Spanish) tool. Results: The Nu-DESC is employed in different contexts such as the adult intensive care unit (ICU), post-anesthetic care unit (PACU), palliative care unit, and hospitalization unit. It is more frequently used in the PACU with a more sensitive threshold (≥ 1); the test showed greater sensitivity of 54.5 % (95 % CI: 32.2–75.6) and specificity of 97.1 % (95 % CI: 95.3–98.4). Conclusion: The Nu-DESC facilitates the recognition of delirium episodes by the nursing team, makes care quicker and individualized for each patient, avoiding immediate pharmacological interventions, and coordinate interdisciplinary actions for diagnosis, especially in post-anesthetic care units.

Effects of a training program improve fitness in children soccer playersEfeitos de um programa de treinamento para melhorar a aptidão em futebolistas crianças