scholarly journals Revisions to the Strategic Petroleum Reserve Well Grading System.

2021 ◽  
Author(s):  
Barry Roberts
Keyword(s):  
Grading System ◽  
Strategic Petroleum Reserve

Developing and Implementing a Specifications Grading System in an Organic Chemistry Laboratory Course

2020 ◽  
Author(s):  
William J. Howitz ◽  
Kate J. McKnelly ◽  
Renee Link
Keyword(s):  
Organic Chemistry ◽  
Chemistry Laboratory ◽  
Grading System ◽  
Traditional Grading ◽  
Final Grade ◽  
Laboratory Courses ◽  
Lower Division ◽  
Collaborative Learning Environment ◽  
Organic Chemistry Laboratory ◽  
Grade Uncertainty

<p>Large, multi-section laboratory courses are particularly challenging when managing grading with as many as 35 teaching assistants (TAs). Traditional grading systems using point-based rubrics lead to significant variations in how individual TAs grade, which necessitates the use of curving across laboratory sections. Final grade uncertainty perpetuates student anxieties and disincentivizes a collaborative learning environment, so we adopted an alternative grading system, called specifications grading. In this system each student knows exactly what level of proficiency they must demonstrate to earn their desired course grade. Higher grades require demonstrating mastery of skills and content at defined higher levels. Each students’ grade is solely dependent on the work they produce rather than the performance of other students. We piloted specifications grading in the smaller, third quarter course of the lower division organic chemistry laboratory series held during a summer term. Open-ended questions were chosen to gather student and TA perceptions of the new grading system. TAs felt that the new grading system reduced the weekly grading time because it was less ambiguous. Responses from students about the nature of the grading system were mixed. Their perceptions indicate that initial buy-in and multiple reminders about the bigger picture of the grading system will be essential to the success of this grading system on a larger scale.</p>

Interrater reliability of a method to assess hypothalamic involvement in pediatric adamantinomatous craniopharyngioma

2020 ◽  
Vol 25 (1) ◽  
pp. 37-42 ◽  
Author(s):  
Ros Whelan ◽  
Eric Prince ◽  
David M. Mirsky ◽  
Robert Naftel ◽  
Aashim Bhatia ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Brain Tumors ◽  
Interrater Reliability ◽  
Statistical Evaluation ◽  
Grading System ◽  
Reliability Study ◽  
Postoperative Mri ◽  
Mri Scans ◽  
Adamantinomatous Craniopharyngioma

OBJECTIVEPediatric adamantinomatous craniopharyngiomas (ACPs) are histologically benign brain tumors that confer significant neuroendocrine morbidity. Previous studies have demonstrated that injury to the hypothalamus is associated with worsened quality of life and a shorter lifespan. This insight helps many surgeons define the goals of surgery for patients with ACP. Puget and colleagues proposed a 3-tiered preoperative and postoperative grading system based on the degree of hypothalamic involvement identified on MRI. In a prospective cohort from their institution, the authors found that use of the system to guide operative goals was associated with decreased morbidity. To date, however, the Puget system has not been externally validated. Here, the authors present an interrater reliability study that assesses the generalizability of this system for surgeons planning initial operative intervention for children with craniopharyngiomas.METHODSA panel of 6 experts, consisting of pediatric neurosurgeons and pediatric neuroradiologists, graded 30 preoperative and postoperative MRI scans according to the Puget system. Interrater reliability was calculated using Fleiss’ κ and Krippendorff’s α statistics.RESULTSInterrater reliability in the preoperative context demonstrated moderate agreement (κ = 0.50, α = 0.51). Interrater reliability in the postoperative context was 0.27 for both methods of statistical evaluation.CONCLUSIONSInterrater reliability for the system as defined is moderate. Slight refinements of the Puget MRI grading system, such as collapsing the 3 grades into 2, may improve its reliability, making the system more generalizable.

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

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