Evidence map›Paper›PMID 42618022›Full record

ArticleTierarztliche Praxis. Ausgabe K, Kleintiere/Heimtiere2026

[Establishment and validation of an LC-MS/MS-based determination method for 17-hydroxyprogesterone in canine serum].

Sebastian Naczenski, Ruth Klein, Andrea Hildebrand

Abstract readValidation StudyEnglish Abstract
In one paragraph

Article in Tierarztliche Praxis. Ausgabe K, Kleintiere/Heimtiere, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sebastian NaczenskiLaboklin GmbH & Co. KG, Bad Kissingen.
Ruth KleinLaboklin GmbH & Co. KG, Bad Kissingen.
Andrea HildebrandLaboklin GmbH & Co. KG, Bad Kissingen.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objective: A reliable determination of steroid hormones such as 17-hydroxyprogesterone has long been restricted by the limitations of immunological methods. Assays such as enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA) or chemiluminescence immunoassay (CLIA) show significant cross-reactions, interferences and pronounced variability depending on the test, which limits their diagnostic value. Background and objective: The aim of this work was the methodological establishment and validation of an LC-MS/MS-based determination method for 17-hydroxyprogesterone in order to provide a specific and robust alternative to immunological test systems. Material and methods: The method was developed using stable isotope standards and included systematic testing of linearity, sensitivity, precision, recovery, and matrix effects. Subsequently, initial canine serum samples were analyzed as part of routine diagnostics to illustrate its applicability in a veterinary setting. Results: The method demonstrated excellent analytical performance with a detection limit of 0.044 ng/ml, high linearity (R²>0.999) and low coefficients of variation (<5%). Cross-reactions, as observed with immunological systems, were ruled out. Conclusion and Clinical Relevance: With the establishment of a LC-MS/MS-based 17-hydroxyprogesterone determination method, a highly specific and precise technique is available, which is in contrast to immunologically based test systems. It thus provides a reliable basis for canine endocrinologic diagnostics and steroid hormone analysis.

Indexed as

17-alpha-HydroxyprogesteroneLiquid Chromatography-Mass SpectrometryTandem Mass SpectrometryAnimalsChromatography, LiquidDogsReproducibility of ResultsSensitivity and Specificity17-alpha-Hydroxyprogesterone

Identifiers

PMID42618022
PMCPMC13652636

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.