Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.

With advent of several treatment options in multiple myeloma, a selection of effective regimen has become an important issue. Use of GEP is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated ability of GEP to predict complete response in MM. GEP from pre-treatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional datasets from three different studies (n= 511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four datasets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56% to 78% in test datasets and no significant difference with regard to GEP platforms, treatment regimens or in newly-diagnosed or relapsed patients. Importantly, permuted p-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.

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Background The coronavirus disease 2019 (COVID-19) pandemic has resulted in rapid and regionally different approaches to breast cancer care. Methods In order to evaluate these changes, a COVID-19-specific registry was developed within the American Society of Breast Surgeons (ASBrS) Mastery that tracked whether decisions were usual or modified for COVID-19. Data on patient care entered into the COVID-19-specific registry and the ASBrS Mastery registry from 1 March 2020 to 15 March 2021 were reviewed. Results Overall, 177 surgeons entered demographic and treatment data on 2791 patients. Mean patient age was 62.7 years and 9.0% (252) were of African American race. Initial consultation occurred via telehealth in 6.2% (173) of patients and 1.4% (40) developed COVID-19. Mean invasive tumor size was 2.1 cm and 17.8% (411) were node-positive. In estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+/HER2−) disease, neoadjuvant endocrine therapy (NET) was used as the usual approach in 6.9% (119) of patients and due to COVID-19 in an additional 31% (542) of patients. Patients were more likely to receive NET due to COVID-19 with increasing age and if they lived in the Northeast or Southeast (odds ratio [OR] 1.1, 2.3, and 1.7, respectively; p < 0.05). Genomic testing was performed on 51.5% (781) of estrogen-positive patients, of whom 20.7% (162) had testing on the core due to COVID-19. Patients were less likely to have core biopsy genomic testing due to COVID-19 if they were older (OR 0.89; p = 0.01) and more likely if they were node-positive (OR 4.0; p < 0.05). A change in surgical approach due to COVID-19 was reported for 5.4% (151) of patients. Conclusion The ASBrS COVID-19 registry provided a platform for monitoring treatment changes due to the pandemic, highlighting the increased use of NET.

Poor preoperative communication can have serious consequences, including unwanted treatment and postoperative conflict.OBJECTIVE To compare the effectiveness of a question prompt list (QPL) intervention vs usual care on patient engagement and well-being among older patients considering major surgery. DESIGN, SETTING, AND PARTICIPANTSThis randomized clinical trial used a stepped-wedge design to randomly assign patients to a QPL intervention (n = 223) or usual care (n = 223) based on the timing of their visit with 1 of 40 surgeons at 5 US study sites. Patients were 60 years or older with at least 1 comorbidity and an oncologic or vascular (cardiac, neurosurgical, or peripheral vascular) problem that could be treated with major surgery. Family members were also enrolled (n = 263). The study dates were June 2016 to November 2018. Data analysis was by intent-to-treat.INTERVENTIONS A brochure of 11 questions to ask a surgeon developed by patient and family stakeholders plus an endorsement letter from the surgeon were sent to patients before their outpatient visit. MAIN OUTCOMES AND MEASURESPrimary patient engagement outcomes included the number and type of questions asked during the surgical visit and patient-reported Perceived Efficacy in Patient-Physician Interactions scale assessed after the surgical visit. Primary well-being outcomes included (1) the difference between patient's Measure Yourself Concerns and Well-being (MYCaW) scores reported after surgery and scores reported after the surgical visit and (2) treatment-associated regret at 6 to 8 weeks after surgery. RESULTSOf 1319 patients eligible for participation, 223 were randomized to the QPL intervention and 223 to usual care. Among 446 patients, the mean (SD) age was 71.8 (7.1) years, and 249 (55.8%) were male. On intent-to-treat analysis, there was no significant difference between the QPL intervention and usual care for all patient-reported primary outcomes. The difference in MYCaW scores for family members was greater in usual care (effect estimate, 1.51; 95% CI, 0.28-2.74; P = .008). When the QPL intervention group was restricted to patients with clear evidence they reviewed the QPL, a nonsignificant increase in the effect size was observed for questions about options (odds ratio, 1.88; 95% CI, 0.81-4.35; P = .16), expectations (odds ratio, 1.59; 95% CI, 0.67-3.80; P = .29), and risks (odds ratio, 2.41; 95% CI, 1.04-5.59; P = .04) (nominal α = .01). CONCLUSIONS AND RELEVANCEThe results of this study were null related to primary patient engagement and well-being outcomes. Changing patient-physician communication may be difficult without addressing clinician communication directly.