Scale errors offer a unique context in which to examine the interdependencies between language and action. Here, we manipulated the presence of labels in a tool-based paradigm previously shown to elicit high rates of scale errors. We predicted that labels would increase children's scale errors with tools by directing attention to shape, function, and category membership. Children between the ages of 2 and 3years were introduced to an apparatus and shown how to produce its function using a tool (e.g., scooping a toy fish from an aquarium using a net). In each of two test trials, children were asked to choose between two novel tools to complete the same task: one that was a large non-functional version of the tool presented in training and one novel functional object (different in shape). A total of four tool-apparatus sets were tested. The results indicated that without labels, scale errors decreased over the two test trials. In contrast, when labels were present, scale errors remained high in the second test trial. We interpret these findings as evidence that linguistic cues can influence children's action-based errors with tools.
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http://dx.doi.org/10.1016/j.jecp.2016.01.007 | DOI Listing |
The shoulder joint complex is prone to musculoskeletal issues, such as rotator cuff-related pain, which affect two-thirds of adults and often result in suboptimal treatment outcomes. Current musculoskeletal models used to understand shoulder biomechanics are limited by challenges in personalization, inaccuracies in predicting joint and muscle loads, and an inability to simulate anatomically accurate motions. To address these deficiencies, we developed a novel, personalized modeling framework capable of calibrating subject-specific joint centers and functional axes for the shoulder complex.
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January 2025
School of Nursing, Chengde Medical University, Chengde, Hebei Province, China.
Background: In China, the rate of exclusive breastfeeding at 6 months is only 29.2%, well below the global breastfeeding collective target of at least 50% by 2025. This study explores the status quo of breastfeeding social support among and analyses its influencing factors, in order to provide a basis for improving breastfeeding rate in China.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Radiology, Stanford University, 1201 Welch Rd, P270, Stanford, California, 94305-6104, UNITED STATES.
Radiation dose and diagnostic image quality are opposing constraints in x-ray CT. Conventional methods do not fully account for organ-level radiation dose and noise when considering radiation risk and clinical task. In this work, we develop a pipeline to generate individualized organ-specific dose and noise at desired dose levels from clinical CT scans.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFHealth Aff (Millwood)
January 2025
Michael E. Chernew, Harvard University.
A core problem with the current risk-adjustment system in Medicare Advantage and accountable care organization (ACO) programs-the Hierarchical Condition Categories (HCC) model-is that the inputs (coded diagnoses) can be influenced for gain by risk-bearing plans or providers. Using existing survey data on health status (which provide less manipulable inputs), we found that the use of a hybrid risk score drawing from survey data and a scaled-back set of HCCs would, in addition to mitigating coding incentives, modestly lessen risk-selection incentives, strengthen payment incentives to deliver efficient care, allocate payment across ACOs more efficiently according to markers of population health that are not as affected by practice patterns or coding efforts, and redistribute payment in a manner that supports equity goals. Although sampling error and survey nonresponse present challenges, analyses suggest that these should not be prohibitive.
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