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Cited by 1,305

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Opioid Abuse in Chronic Pain — Misconceptions and Mitigation Strategies

Volkow, Nora D., McLellan, A. Thomas·2016·New England Journal of Medicine

Abstract

Chronic pain not caused by cancer is among the most prevalent and debilitating medical conditions but also among the most controversial and complex to manage. The urgency of patients’ needs, the demonstrated effectiveness of opioid analgesics for the management of acute pain, and the limited therapeutic alternatives for chronic pain have combined to produce an overreliance on opioid medications in the United States, with associated alarming increases in diversion, overdose, and addiction. Given the lack of clinical consensus and research-supported guidance, physicians understandably have questions about whether, when, and how to prescribe opioid analgesics for chronic pain without increasing public health risks. Here, we draw on recent research to address common misconceptions regarding the abuse-related risks of opioid analgesics and highlight strategies to minimize those risks.

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Metadata

Authors
Volkow, N. D., & McLellan, A. T.
Venue
New England Journal of Medicine
Published
2016-03-31
Volume
374
Issue
13
Pages
1253-1263
ISSN
  • 0028-4793 (ISSN-L)
  • 1533-4406
OpenAlex
W2329316201
Cited by
1,305
References
13
FWCI
153.86
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Opioid Abuse in Chronic Pain — Misconceptions and Mitigation Strategies. https://app.cochat.ai/papers/10.1056/nejmra1507771
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Volkow, N. D., & McLellan, A. T. (2016). Opioid Abuse in Chronic Pain — Misconceptions and Mitigation Strategies. New England Journal of Medicine. https://doi.org/10.1056/nejmra1507771

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.