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Cited by 264

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Steroid Hormone Analysis by Tandem Mass Spectrometry

Open access
Soldin, Steven J, Soldin, Offie P·2009·Clinical Chemistry

Abstract

Background: New high-performance liquid chromatography/tandem mass spectrometry (LC-MS/MS) methods are among the most successful approaches to improve specificity problems inherent in many immunoassays. Content: We emphasize problems with immunoassays for the measurement of steroids and review the emerging role of LC-MS/MS in the measurement of clinically relevant steroids. The latest generation of tandem mass spectrometers has superior limits of quantification, permitting omission of previously employed derivatization steps. The measurement of steroid profiles in the diagnosis and treatment of congenital adrenal hyperplasia, adrenal insufficiency, chronic pelvic pain and prostatitis, oncology (breast cancer), and athletes has important new applications. Conclusions: LC-MS/MS now affords the specificity, imprecision, and limits of quantification necessary for the reliable measurement of steroids in human fluids, enhancing diagnostic capabilities, particularly when steroid profiles are available.

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Crossref · 10.1373/clinchem.2007.100008

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Metadata

Authors
Soldin, S. J., & Soldin, O. P.
Venue
Clinical Chemistry
Published
2009-06-01
Volume
55
Issue
6
Pages
1061-1066
ISSN
  • 0009-9147 (ISSN-L)
  • 1530-8561
OpenAlex
W2102203236
Cited by
264
References
44
FWCI
18.05
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Steroid Hormone Analysis by Tandem Mass Spectrometry. https://app.cochat.ai/papers/10.1373/clinchem.2007.100008
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Soldin, S. J., & Soldin, O. P. (2009). Steroid Hormone Analysis by Tandem Mass Spectrometry. Clinical Chemistry. https://doi.org/10.1373/clinchem.2007.100008

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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.