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The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

The Prediction of Suicide: Sensitivity, Specificity, and Predictive Value of a Multivariate Model Applied to Suicide Among 1906 Patients With Affective Disorders

Goldstein, Rise B.·1991·Archives of General Psychiatry

Abstract

Stepwise multiple logistic regression was utilized in an attempt to develop a statistical model that would predict suicide in a group of 1906 Iowans with affective disorders admitted to a tertiary care hospital. The risk factors identified by this approach included the number of prior suicide attempts, suicidal ideation on admission, bipolar affective disorder (manic or mixed type), gender, outcome at discharge, and unipolar depressive disorder in individuals with a family history of mania. However, the model failed to identify any of the patients who committed suicide. The results appear to support the contention that, based on present knowledge, it is not possible to predict suicide, even among a high-risk group of inpatients.

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Crossref · 10.1001/archpsyc.1991.01810290030004

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Metadata

Authors
Goldstein, R. B.
Venue
Archives of General Psychiatry
Published
1991-05-01
Volume
48
Issue
5
Pages
418
ISSN
0003-990X (ISSN-L)
OpenAlex
W2074042904
Cited by
402
References
1
FWCI
14.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Prediction of Suicide: Sensitivity, Specificity, and Predictive Value of a Multivariate Model Applied to Suicide Among 1906 Patients With Affective Disorders. https://app.cochat.ai/papers/10.1001/archpsyc.1991.01810290030004
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Goldstein, R. B. (1991). The Prediction of Suicide: Sensitivity, Specificity, and Predictive Value of a Multivariate Model Applied to Suicide Among 1906 Patients With Affective Disorders. Archives of General Psychiatry. https://doi.org/10.1001/archpsyc.1991.01810290030004

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.