Dataset Information

Nucleophile-intercepted Beckmann fragmentation reactions.

ABSTRACT: We describe the first examples of nucleophile-intercepted Beckmann fragmentations of indoline oximes. This reaction uses MsCl as a promoter to give cyano chlorides and is believed to proceed through an aziridinium intermediate via a double stereoinvertive process. Mechanistic insights have led to the further discovery that oxygen, nitrogen, and bromide nucleophiles can be employed for this fragmentation by the use of other promoters. We envision that these products may be useful in the syntheses of members of the akuammiline and koumine families of indoline alkaloids.

SUBMITTER: Touchette SJ

PROVIDER: S-EPMC6761917 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient, prescription, home medication by history), and order content. We compared logistic regression, random forest, boosted decision trees, and artificial neural network models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The dataset included 5,804,192 medication orders, of which 28,695 (0.5%) were voided. ML correctly classified voids at reasonable accuracy; with a positive predictive value of 10%, ~20% of errors were included. Gradient boosted decision trees achieved the highest AUROC (0.7968) and AUPRC (0.0647) among all models. Logistic regression had the poorest performance. Models identified predictive factors with high face validity (e.g., student orders), and a decision tree revealed interacting contexts with high rates of errors not identified by previous regression models. Prediction models using order-entry information offers promise for error surveillance, patient safety improvements, and targeted clinical review. The improved performance of models with complex interactions points to the importance of contextual medication ordering information for understanding contributors to medication errors.

Dataset Information

Nucleophile-intercepted Beckmann fragmentation reactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure