UTCPredictor: An uncertainty‐aware novel teaching cases predictor

Wu, Xiaoxue; Zheng, Wei; Mu, Dejun; Li, Ning

doi:10.1002/cae.22166

Cited by 5 publications

(2 citation statements)

References 25 publications

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“… Table based on [ 210 , 213 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 ]. …”

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Analysis of the Use of Sample Size and Effect Size Calculations in a Temporomandibular Disorders Randomised Controlled Trial—Short Narrative Review

Zieliński,

Gawda

2024

JPM

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Background/Objectives: Temporomandibular disorder (TMD) is the term used to describe a pathology (dysfunction and pain) in the masticatory muscles and temporomandibular joint (TMJ). There is an apparent upward trend in the publication of dental research and a need to continually improve the quality of research. Therefore, this study was conducted to analyse the use of sample size and effect size calculations in a TMD randomised controlled trial. Methods: The period was restricted to the full 5 years, i.e., papers published in 2019, 2020, 2021, 2022, and 2023. The filter article type—“Randomized Controlled Trial” was used. The studies were graded on a two-level scale: 0–1. In the case of 1, sample size (SS) and effect size (ES) were calculated. Results: In the entire study sample, SS was used in 58% of studies, while ES was used in 15% of studies. Conclusions: Quality should improve as research increases. One factor that influences quality is the level of statistics. SS and ES calculations provide a basis for understanding the results obtained by the authors. Access to formulas, online calculators and software facilitates these analyses. High-quality trials provide a solid foundation for medical progress, fostering the development of personalized therapies that provide more precise and effective treatment and increase patients’ chances of recovery. Improving the quality of TMD research, and medical research in general, helps to increase public confidence in medical advances and raises the standard of patient care.

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“… Table based on [ 210 , 213 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 ]. …”

Section: Figureunclassified

Analysis of the Use of Sample Size and Effect Size Calculations in a Temporomandibular Disorders Randomised Controlled Trial—Short Narrative Review

Zieliński,

Gawda

2024

JPM

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“…To measure the effect size, we used the Cliff's Delta statistic with 0.147, 0.33, and 0.474 as cutoffs for small, medium, and large effect size, respectively. 23 Receiver operator characteristic curves were used to assess the diagnostic characteristics of TIC parameters. Differences were considered significant at P < 0.05.…”

Section: Blood and Histopathological Examinations And Patient Dispositionmentioning

confidence: 99%

Quantitative contrast‐enhanced ultrasound for the differentiation of kidney allografts with significant histopathological injury

et al. 2021

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Objective: To identify specific quantitative contrast-enhanced ultrasound (CEUS) parameters that could distinguish kidney transplants with significant histopathological injury.Methods: Sixty-four patients were enrolled in this prospective observational study.Biopsies were performed following CEUS and blood examination.Results: 28 biopsy specimens had minimal changes (MC group), while 36 had significant injury (SI group). Of these, 12 had rejection (RI group) and 24 non-rejection injury (NRI group). In RI and NRI groups, temporal difference in time to peak (TTP) between medulla and cortex (ΔTTPm-c) was significantly shorter compared to the MC group (5.77, 5.92, and 7.94 s, P = 0.048 and 0.026, respectively). Additionally, RI group had significantly shorter medullary TTP compared to the MC group (27.75 vs. 32.26 s; P = 0.03). In a subset of 41 patients with protocol biopsy at 1-year post-transplant, ΔTTPm-c was significantly shorter in the SI compared to the MC group (5.67 vs. 7.67 s; P = 0.024). Area under receiver operating characteristic curves (AUROCs) for ΔTTPm-c was 0.69 in all patients and 0.71 in patients with protocol biopsy.Conclusions: RI and NRI groups had shorter ΔTTPm-c compared to the MC group.AUROCs for both patient groups were good, making ΔTTPm-c a promising CEUS parameter for distinguishing patients with significant histopathological injury.

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A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings

Hahn

Navarro

de-la-Fuente-Valentín

et al. 2021

IEEE Access

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Automatic scoring and feedback tools have become critical components of online learning proliferation. These tools range from multiple-choice questions to grading essays using machine learning (ML). Learning environments such as massive open online courses (MOOCs) would not be possible without them. The usage of this mechanism has brought many exciting areas of study, from the design of questions to the ML grading tools' precision and accuracy. This paper analyzes the findings of 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool. This analysis gives an overview of the trends, challenges, and open questions in this research area. The results indicate that automatic scoring and feedback have many advantages. The most important benefits include enabling scaling the number of students without adding a proportional number of instructors, improving the student experience by reducing the time between submission grading and feedback, and removing bias in scoring.On the other hand, these technologies have some drawbacks. The main problem is creating a disincentive to develop innovative answers that do not match the expected one or have not been considered when preparing the problem. Another drawback is potentially training the student to answer the question instead of learning the concepts. With this, given the exitance of a correct answer, such an answer could be leaked to the internet, making it easier for students to avoid solving the problem. Overall, each of these drawbacks presents an opportunity to look at ways to improve technologies to use these tools to provide a better learning experience to students.

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UTCPredictor: An uncertainty‐aware novel teaching cases predictor

Cited by 5 publications

References 25 publications

Analysis of the Use of Sample Size and Effect Size Calculations in a Temporomandibular Disorders Randomised Controlled Trial—Short Narrative Review

Analysis of the Use of Sample Size and Effect Size Calculations in a Temporomandibular Disorders Randomised Controlled Trial—Short Narrative Review

Quantitative contrast‐enhanced ultrasound for the differentiation of kidney allografts with significant histopathological injury

A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings

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