At an annual cost of roughly $7 billion nationally, remedial coursework is one of the single largest interventions intended to improve outcomes for underprepared college students. But like a costly medical treatment with non-trivial side effects, the value of remediation overall depends upon whether those most likely to benefit can be identified in advance. Our analysis uses administrative data and a rich predictive model to examine the accuracy of remedial screening tests, either instead of or in addition to using high school transcript data to determine remedial assignment. We find that roughly one in four test-takers in math and one in three test-takers in English are severely mis-assigned under current test-based policies, with mis-assignments to remediation much more common than mis-assignments to college-level coursework. We find that using high school transcript information-either instead of or in addition to test scores-could significantly reduce the prevalence of assignment errors. Further, we find that the choice of screening device has significant implications for the racial and gender composition of both remedial and college-level courses. Finally, we find that if institutions took account of students' high school performance, they could remediate substantially fewer students without lowering success rates in college-level courses.

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At an annual cost of roughly $7 billion nationally, remedial coursework is one of the single largest interventions intended to improve outcomes for underprepared college students. But like a costly medical treatment with non-trivial side effects, the value of remediation overall depends upon whether those most likely to benefit can be identified in advance. Our analysis uses administrative data and a rich predictive model to examine the accuracy of remedial screening tests, either instead of or in addition to using high school transcript data to determine remedial assignment. We find that roughly one in four test-takers in math and one in three test-takers in English are severely mis-assigned under current test-based policies, with mis-assignments to remediation much more common than mis-assignments to college-level coursework. We find that using high school transcript information-either instead of or in addition to test scores-could significantly reduce the prevalence of assignment errors. Further, we find that the choice of screening device has significant implications for the racial and gender composition of both remedial and college-level courses. Finally, we find that if institutions took account of students' high school performance, they could remediate substantially fewer students without lowering success rates in college-level courses.

ObjectiveTo develop and validate a clinical prediction model for antiepileptic drug-resistant (AED-resistant) genetic generalized epilepsy (GGE).MethodWe performed a case-control study of patients with and without drug-resistant GGE, nested within ongoing longitudinal observational studies of AED response at 2 tertiary epilepsy centers. Using a validation dataset, we tested the predictive performance of 3 candidate models, developed from a training dataset. We then tested the candidate models' predictive ability on an external testing dataset.ResultsOf 5,189 patients in the ongoing longitudinal study, 122 met criteria for AED-resistant GGE and 468 met criteria for AED-responsive GGE. There were 66 GGE patients in the external dataset, of whom 17 were cases. Catamenial epilepsy, history of a psychiatric condition, and seizure types were strongly related with drug-resistant GGE case status. Compared to women without catamenial epilepsy, women with catamenial epilepsy had about a four-fold increased risk for AED-DR. The calibration of 3 models, assessing the agreement between observed outcomes and predictions, was adequate. Discriminative ability, as measured with area under the ROC curve (AUC) ranged 0.58–0.65.ConclusionCatamenial epilepsy, history of a psychiatric condition, and the seizure type combination of generalized tonic clonic, myoclonic, and absence seizures are negative prognostic factors of drug-resistant GGE. The AUC of 0.6 is not consistent with truly effective separation of the groups, suggesting other unmeasured variables may need to be considered in future studies to improve predictability.