The association of age, literacy, and race on completing patient-reported outcome measures in pediatric oncology.

PURPOSE: Age is often used to determine when children can begin completing patient-reported outcome (PRO) instruments or transition to adult instruments. This study's purpose was to determine relationships between literacy, age, and race and their influence on a child's ability to understand and complete a PRO instrument. METHODS: The Wide Range Achievement Test was used to evaluate literacy in children and young adults with cancer, participating in a cognitive interview for the Pediatric PRO-CTCAE instrument. 140 participants (7-20 years) were recruited from 8 sites. Logistic regression and multivariable liner regression were used to examine relationships among key variables. RESULTS: Higher literacy scores were significantly associated with fewer PRO-CTCAE items being identified as "hard to understand" (p = 0.017). Literacy scores increased with age, but older participants were more likely to fall behind expected reading levels compared with US norms. A 1-year increase in age was associated with a 19% increase in the likelihood for being below the expected WRAT word reading score (OR 1.19; 95% CI 1.06-1.33, p = 0.003). No associations were found between race and literacy. CONCLUSIONS: Wide variations in literacy were noted across age groups. All participants were able to complete the Pediatric PRO-CTCAE, although most 7 year olds (63%) required reading assistance. Those with lower literacy skills were able to understand items suggesting that multiple factors may be involved in comprehension (developmental stage, concentration, vocabulary, or prior health experiences). Risk for falling below expected literacy levels increased with age implying a need for literacy consideration for cancer patients.

High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation.

Wine growers prefer cultivars with looser bunch architecture because of the decreased risk for bunch rot. As a consequence, grapevine breeders have to select seedlings and new cultivars with regard to appropriate bunch traits. Bunch architecture is a mosaic of different single traits which makes phenotyping labor-intensive and time-consuming. In the present study, a fast and high-precision phenotyping pipeline was developed. The optical sensor Artec Spider 3D scanner (Artec 3D, L-1466, Luxembourg) was used to generate dense 3D point clouds of grapevine bunches under lab conditions and an automated analysis software called 3D-Bunch-Tool was developed to extract different single 3D bunch traits, i.e., the number of berries, berry diameter, single berry volume, total volume of berries, convex hull volume of grapes, bunch width and bunch length. The method was validated on whole bunches of different grapevine cultivars and phenotypic variable breeding material. Reliable phenotypic data were obtained which show high significant correlations (up to r² = 0.95 for berry number) compared to ground truth data. Moreover, it was shown that the Artec Spider can be used directly in the field where achieved data show comparable precision with regard to the lab application. This non-invasive and non-contact field application facilitates the first high-precision phenotyping pipeline based on 3D bunch traits in large plant sets.