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Cited by 308

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

A Review of Opioid-Sparing Modalities in Perioperative Pain Management: Methods to Decrease Opioid Use Postoperatively

Kumar, Kanupriya, Kirksey, Meghan A., Duong, Silvia, Wu, Christopher L.·2017·Anesthesia & Analgesia

Abstract

There is an epidemic of opioid use, abuse, and misuse in the United States, which results in significant morbidity and mortality. It may be difficult to reduce perioperative opioid use given known acute surgical trauma and resultant pain; however, the discrete and often limited nature of postoperative pain also may make management easier in part by utilizing nonopioid modalities, such as regional anesthesia/analgesia, and multimodal analgesia, which may decrease the need for powerful opioids. This article reviews the relevant literature describing the use of adjunct medications, regional anesthesia and analgesic techniques, and regional block additives in the context of providing adequate pain control while lessening opioid use.

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Crossref · 10.1213/ane.0000000000002497

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Metadata

Authors
Kumar, K., Kirksey, M. A., Duong, S., & Wu, C. L.
Venue
Anesthesia & Analgesia
Published
2017-11
Volume
125
Issue
5
Pages
1749-1760
ISSN
0003-2999 (ISSN-L)
OpenAlex
W2765305386
Cited by
308
References
89
FWCI
28.81
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Review of Opioid-Sparing Modalities in Perioperative Pain Management: Methods to Decrease Opioid Use Postoperatively. https://app.cochat.ai/papers/10.1213/ane.0000000000002497
The paper
Kumar, K., Kirksey, M. A., Duong, S., & Wu, C. L. (2017). A Review of Opioid-Sparing Modalities in Perioperative Pain Management: Methods to Decrease Opioid Use Postoperatively. Anesthesia & Analgesia. https://doi.org/10.1213/ane.0000000000002497

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.