Quest for Knowledge and Pursuit of Grades: Information, Course Selection, and Grade Inflation at an Ivy League School

This paper exploits a unique natural experiment — Cornell University’s 1996 decision to publish course median grades online - to examine the effect of grade information on course selection and grade inflation. We model students’ course selection as dependent on their tastes, abilities, and expected grades. The model yields three testable hypotheses: (1) students will tend to be drawn to leniently graded courses once exposed to grade information; (2) the most talented students will be less drawn to leniently graded courses than their peers; (3) the change in students’ behavior will contribute to grade inflation. Examining a large dataset that covers the period 1990-2004 our study provides evidence consistent with these predictions

Interdigitation and Ellipsoid Zones Disruption Correlate with Visual Outcomes among Treatment-Naive Patients with Diabetic Macular Edema

Publisher Copyright: © 2020 The Author(s). Published by S. Karger AG, Basel.Introduction: We have recently shown that defects in interdigitation and ellipsoid zones (IZ and EZ) can predict response to anti-VEGF therapy in a small group of treatment-naive diabetic macular edema (DME) patients. The aim of the current study is to further evaluate this association in a larger study group of patients over a longer follow-up time. Methods: Thirty eyes of 30 treatment-naive DME patients were analyzed in this retrospective study. The integrity of foveal IZ and EZ was evaluated using optical coherence tomography at the diagnosis of DME and following anti-VEGF injections. The defect size was correlated with best-corrected visual acuity (BCVA) and central macular thickness (CMT). Results: The mean patients' age at baseline was 63.0 ± 10.0 years. Patients underwent 3.9 ± 2.9 anti-VEGF injections for a mean of 9.1 ± 4.8 months. Following treatment, the mean Snellen visual acuity (VA) improved from 20/52 to 20/44 (p = 0.05), CMT decreased from 432.5 ± 141.4 μm to 375.2 ± 121.4 μm (p = 0.05) and IZ/EZ defect size decreased from 259.83 ± 375.94 μm to 65.34 ± 143.97 μm (p = 0.001). In patients with no IZ/EZ defects at baseline, the mean Snellen VA was better when compared to those with IZ/EZ defects (20/36 vs. 20/70, p = 0.031). The number of eyes with IZ/EZ defects decreased from 17 (57%) at baseline to 6 (20%) at end of follow-up (p < 0.01). BCVA gain correlated with IZ/EZ defect size reduction (r = 0.41, p = 0.02) but not with improvement in CMT (r = 0.28, p = 0.121). Conclusions: IZ/EZ defect size correlated not only with baseline BCVA but also predicted the change in BCVA after anti-VEGF treatment. Possible future automatic measurement of IZ/EZ defect size might prove helpful for the evaluation of treatment response.Peer reviewe

Discovery of microRNAs and other small RNAs in solid tumors

MicroRNAs (miRNAs) are ∼22-nt long, non-coding RNAs that regulate gene silencing. It is known that many human miRNAs are deregulated in numerous types of tumors. Here we report the sequencing of small RNAs (17–25 nt) from 23 breast, bladder, colon and lung tumor samples using high throughput sequencing. We identified 49 novel miRNA and miR-sized small RNAs. We further validated the expression of 10 novel small RNAs in 31 different types of blood, normal and tumor tissue samples using two independent platforms, namely microarray and RT–PCR. Some of the novel sequences show a large difference in expression between tumor and tumor-adjacent tissues, between different tumor stages, or between different tumor types. We also report the identification of novel small RNA classes in human: highly expressed small RNA derived from Y-RNA and endogenous siRNA. Finally, we identified dozens of new miRNA sequence variants that demonstrate the existence of miRNA-related SNP or post-transcriptional modifications. Our work extends the current knowledge of the tumor small RNA transcriptome and provides novel candidates for molecular biomarkers and drug targets

Habermas and Public Reason in the Digital Age: Technology and Deliberative Democracy

Scholars defending the deliberative model of democracy have focused much of their attention on argumentation and criteria for offering public reasons in deliberative processes, but have paid little attention to the ways in which digital technologies mediate such deliberations. Conversely, critical theorists of technology have emphasized the socially determined nature of technology, but have lacked a theory of democracy through which to normatively assess technologies that mediate public discourse. Through a reworking of Jürgen Habermas’s discourse-based theory of democracy, my research provides a new understanding of the flows of political communication and power in the democratic public sphere and the implications of digital technologies for democratic participation

Partisan Grading

We study grading outcomes associated with professors in an elite university in the United States who were identified—using voter registration records from the county where the university is located—as either Republicans or Democrats. The evidence suggests that student grades are linked to the political orientation of professors. Relative to their Democratic colleagues, Republican professors are associated with a less egalitarian distribution of grades and with lower grades awarded to black students relative to whites. (JEL D72, I23, J15)

Abstract

This paper exploits a unique natural experiment — Cornell University’s 1996 decision to publish course median grades online — to examine the effect of grade information on course selection and grade inflation. We model students ’ course selection as dependent on their tastes, abilities, and expected grades. The model yields three testable hypotheses: (1) students will tend to be drawn to leniently graded courses once exposed to grade information; (2) the most talented students will be less drawn to leniently graded courses than their peers; (3) the change in students ’ behavior will contribute to grade inflation. Examining a large dataset that covers the period 1990-2004, our study provides evidence consistent with these predictions

Development of "Predict ME," an online classifier to aid in differentiating diabetic macular edema from pseudophakic macular edema.

PURPOSE Differentiating the underlying pathology of macular edema in patients with diabetic retinopathy following cataract surgery can be challenging. In 2015, Munk and colleagues trained and tested a machine learning classifier which uses optical coherence tomography variables in order to distinguish the underlying pathology of macular edema between diabetic macular edema and pseudophakic cystoid macular edema. It was able to accurately diagnose the underlying pathology in 90%-96% of cases. However, actually using the trained classifier required dedicated software and advanced technical skills which hindered its accessibility to most clinicians. Our aim was to package the classifier in an easy to use web-tool and validate the web-tool using a new cohort of patients. METHODS We packaged the classifier in a web-tool intended for use on a personal computer or mobile phone. We first ensured that the results from the web-tool coincide exactly with the results from the original algorithm and then proceeded to test it using data of 14 patients. RESULTS The etiology was accurately predicted in 12 out of 14 cases (86%). The cases with diabetic macular edema were accurately diagnosed in 7 out of 7 cases. Of the pseudophakic cystoid macular edema cases, 5 out of 6 were correctly interpreted and 1 case with a mixed etiology was interpreted as pseudophakic cystoid macular edema. Variable input was reported to be easy and took on average 7 ± 3 min. CONCLUSION The web-tool implementation of the classifier seems to be a valuable tool to support research into this field

OPTICAL COHERENCE TOMOGRAPHY BIOMARKERS TO DISTINGUISH DIABETIC MACULAR EDEMA FROM PSEUDOPHAKIC CYSTOID MACULAR EDEMA USING MACHINE LEARNING ALGORITHMS.

PURPOSE In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, identifying the underlying pathology can be challenging and influence management. Our aim was to develop a simple clinical classifier able to confirm a diabetic etiology using few spectral domain optical coherence tomography parameters. METHODS We analyzed spectral domain optical coherence tomography data of 153 patients with either pseudophakic cystoid ME (n = 57), diabetic ME (n = 86), or "mixed" (n = 10). We used advanced machine learning algorithms to develop a predictive classifier using the smallest number of parameters. RESULTS Most differentiating were the existence of hard exudates, hyperreflective foci, subretinal fluid, ME pattern, and the location of cysts within retinal layers. Using only 3 to 6 spectral domain optical coherence tomography parameters, we achieved a sensitivity of 94% to 98%, specificity of 94% to 95%, and an area under the curve of 0.937 to 0.987 (depending on the method) for confirming a diabetic etiology. A simple decision flowchart achieved a sensitivity of 96%, a specificity of 95%, and an area under the curve of 0.937. CONCLUSION Confirming a diabetic etiology for edema in cases with uncertainty between diabetic cystoid ME and pseudophakic ME was possible using few spectral domain optical coherence tomography parameters with high accuracy. We propose a clinical decision flowchart for cases with uncertainty, which may support the decision for intravitreal injections rather than topical treatment

Grade Information and Grade Inflation: The Cornell Experiment

Grade inflation and high grade levels have been subjects of concern and public debate in recent decades. In the mid-1990s, Cornell University's Faculty Senate had a number of discussions about grade inflation and what might be done about it. In April 1996, the Faculty Senate voted to adopt a new grade reporting policy which had two parts: 1) the publication of course median grades on the Internet; and 2) the reporting of course median grades in students' transcripts. The policy change followed the determination of a university committee that "it is desirable for Cornell University to provide more information to the reader of a transcript and produce more meaningful letter grades." It was hoped that "More accurate recognition of performance may encourage students to take courses in which the median grade is relatively low." The median grade policy has remained to date only partially implemented: median grades have been reported online since 1998 but do not yet appear in transcripts. We evaluate the effect of the implemented policy on patterns of course choice and grade inflation. Specifically, we test two related hypotheses: First, all else being equal, the availability of online grade information will lead to increased enrollment into leniently graded courses. Second, high-ability students will be less attracted to the leniently graded courses than their peers. Building on these results we perform an exercise that identifies the extent to which the change in student behavior resulted in an increase in the university-wide mean grade.