Dataset Information

Intercepted Decarboxylative Allylations of Nitroalkanoates.

ABSTRACT: Using palladium-catalyzed decarboxylation, several cascade reactions of allyl and prenyl nitroalkanoates that lead to nitro-containing chemical building blocks are described. A nitronate Michael addition/Tsuji-Trost allylation cascade was developed, leading to functionally dense chemical building blocks. Likewise, a Tsuji-Trost/decarboxylative protonation sequence was developed for the synthesis of orthogonally functionalized 2° nitroalkanes. The latter method provides rapid access to the indolizidine core.

SUBMITTER: Schmitt M

PROVIDER: S-EPMC3430473 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Intercepted Decarboxylative Allylations of Nitroalkanoates.

Schmitt Meghan M Grenning Alexander J AJ Tunge Jon A JA

Tetrahedron letters 20120607 34

Using palladium-catalyzed decarboxylation, several cascade reactions of allyl and prenyl nitroalkanoates that lead to nitro-containing chemical building blocks are described. A nitronate Michael addition/Tsuji-Trost allylation cascade was developed, leading to functionally dense chemical building blocks. Likewise, a Tsuji-Trost/decarboxylative protonation sequence was developed for the synthesis of orthogonally functionalized 2° nitroalkanes. The latter method provides rapid access to the indoli ...[more]

PMID: 22942479

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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

Intercepted Decarboxylative Allylations of Nitroalkanoates.

Publications

Intercepted Decarboxylative Allylations of Nitroalkanoates.

Similar Datasets

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