Instructor evaluation of progressive student skills in the analysis of primary literature is critical for the development of these skills in young scientists. Students in a senior or graduate-level one-semester course in Immunology at a Masters-level comprehensive university were assessed for abilities (primary traits) to recognize and evaluate the following elements of a scientific paper: Hypothesis and Rationale, Significance, Methods, Results, Critical Thinking and Analysis, and Conclusions. We tested the hypotheses that average recognition scores vary among elements and that scores change with time differently by trait. Recognition scores (scaled 1 to 5), and differences in scores were analyzed using analysis of variance (ANOVA), regression, and analysis of covariance (ANCOVA) (n = 10 papers over 103 days). By multiple comparisons testing, we found that recognition scores statistically fell into two groups: high scores (for Hypothesis and Rationale, Significance, Methods, and Conclusions) and low scores (for Results and Critical Thinking and Analysis). Recognition scores only significantly changed with time (increased) for Hypothesis and Rationale and Results. ANCOVA showed that changes in recognition scores for these elements were not significantly different in slope (F1,16 = 0.254, P = 0.621) but the Results trait was significantly lower in elevation (F1,17 = 12.456, P = 0.003). Thus, students improved with similar trajectories, but starting and ending with lower Results scores. We conclude that students have greatest difficulty evaluating Results and critically evaluating scientific validity. Our findings show extant student skills, and the significant increase in some traits shows learning. This study demonstrates that students start with variable recognition skills and that student skills may be learned at differential rates. Faculty can use these findings or the primary trait analysis scoring scale to focus on specific paper elements for which they desire to improve recognition.
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http://dx.doi.org/10.1128/me.6.1.20-27.2005 | DOI Listing |
Dig Dis Sci
January 2025
Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, Tainan, 70401, Taiwan.
Aim: Sarcopenic obesity (SO) is associated with adverse outcomes in diseased patients. This study aimed to examine the prevalence and risks associated with SO, with a focus on the impact of SO on cardiovascular risk in patients with MASLD.
Materials And Methods: In this cross-sectional study, patients with MASLD were prospectively enrolled.
J Neuroeng Rehabil
January 2025
Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Vita Stråket 12, Floor 4, 41346, Gothenburg, Sweden.
Background: Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This study aims to determine the feasibility and preliminary effectiveness of a novel intervention combining MPR, virtual reality (VR), and serious gaming to improve upper limb function in people with chronic stroke.
Methods: In this single case experimental A-B-A design study, six individuals with chronic stroke and moderate to severe upper limb impairment completed 18, 2 h sessions, 3 times a week.
Sci Rep
January 2025
University Institute of Computing, Chandigarh University, Punjab, India.
Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with "curse of dimensionality" due to excessive features resulting in prolonged training time and exhaustive computational demand. This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue.
View Article and Find Full Text PDFJ Hand Ther
January 2025
Saint Joseph, MI, USA. Electronic address:
Background: For patients who experience atypical neurogenic pain thought to be complex regional pain syndrome (CRPS) after Dupuytren's fasciectomy early recognition has been reported to improve outcomes. Furthermore, given the progressive nature of Dupuytren's, individuals with a history of CRPS have been "at risk" for further surgical intervention.
Purpose: To familiarize therapists with a Budapest criteria (BC) checklist for early diagnosis of CRPS, describe how tracking sudomotor/vasomotor signs alongside differences in skin temperature were used to monitor vasomotor instability and intervention effectiveness for a patient with atypical pain after fasciectomy and to detail management of the same patient with a CRPS history who had collagenase clostridium histolyticum (CCH) injection of her other hand without exacerbating CRPS.
PLoS One
January 2025
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nadu, India.
In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.
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