Search

Aims: Multidisciplinary meetings (MDMs) play a crucial role in decision‐making in breast cancer patient care. This study aimed to firstly assess the impact of breast cancer MDMs in decision‐making for breast cancer patients and secondly to determine the concordance between MDM recommendations and implementation of clinical practice. Methods: Patient cases to be presented at the weekly breast cancer MDMs were identified and prospectively enrolled. Management plans were predicted by the treating surgeon with the pre‐MDM management plans then compared to MDM recommendations. Changes in decision‐making were assessed in the following domains: further surgery, systemic therapy (endocrine, chemotherapy or targeted), radiotherapy, enrolment in a clinical trial, further investigations, and referral to other specialists or services. Patient records were subsequently reviewed at 3 months post‐MDM to assess the rate of implementation of MDM recommendations and any reasons for discordance. Results: Out of 50 cases, 66% (CI 53–79%; p <.005) experienced a change in management plan as a result of MDM discussion, with a total of 66 episodes of recorded change per decision‐making domain affecting the following: further surgery (7.6%), endocrine therapy (4.5%), chemotherapy (19.7%), targeted therapy (4.5%), radiotherapy (18.2%), enrolment for a clinical trial (12.1%), additional investigations (22.7%), and further referrals (10.6%). MDM recommendations were implemented in 83.7% of cases. Conclusion: The breast cancer MDMs were found to substantially impact on the management plans for breast cancer patients, with 83.7% of MDM recommendations being implemented into clinical practice. This study reinforces the importance of MDMs in the management of these patients, as well as highlighting the need for further investigating and addressing the potential barriers to the implementation of MDM recommendations. [ABSTRACT FROM AUTHOR]

This study aims to investigate consumer acceptance of gene‐edited orange juice, focusing on the influence of behavioral traits. Specifically, we conducted an online survey of US orange juice consumers, using a choice experiment to elicit consumers' preferences for gene editing (GE) versus gene modification (GM) in orange juice. We find that consumers require a significantly larger discount for GM than for GE, and this preference for GE is influenced by multiple behavioral traits. We used a latent cla4ss approach to further analyze choice behavior among consumer segments. Findings indicate significant heterogeneity in preferences across consumer segments, which can be explained by behavioral traits and individual‐specific characteristics. [ABSTRACT FROM AUTHOR]

A novel way to apply machine learning algorithms on the incremental capacity analysis (dQ/dV) is developed to identify battery cycling conditions under different temperatures and working SOC ranges. Batteries are cycled under each combination of temperatures (-10oC, 25oC, 60oC) and SOC ranges (0-10%, 25-75%, 90-100%, 0-100%) up to 60 equivalent cycles. The discharge data is transformed into dQ/dV-V curve and its features of the peaks and valleys are further taken for machine learning. Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range.