Evidence map›Paper›PMID 35966261›Full record

ArticleBiochemia medica2022

A practical approach example to measurement uncertainty: Evaluation of 26 immunoassay parameters.

Rabia Tan, Mustafa Yilmaz, Yusuf Kurtulmuş

Abstract read
In one paragraph

Article in Biochemia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
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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.

Rabia TanDepartment of Clinical Biochemistry, Aydın Public Health Laboratory, Aydın, Turkey.
Mustafa YilmazDepartment of Medical Biochemistry, Aydın Adnan Menderes University Faculty of Medicine, Aydın, Turkey.
Yusuf KurtulmuşDepartment of Addiction Toxicology, Institute of Substance Abuse Toxicology and Pharmaceutical Sciences, Ege University, İzmir, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Measurement uncertainty is a non-negative parameter that characterizes the distribution of all values appropriate to the measured size and is associated with the measured result. In this study, we aimed to compare the results with various suggestions and produce more qualified results by calculating the measurement uncertainties of the immunoassays like fertility hormones, drug concentration tests, cardiac markers, thyroid function tests and tumour markers. Materials and methods: Uncertainty calculation was made in accordance with the top-down approach according to Nordtest guide. The 12-month study of internal and external quality assessment results were used. The parameters of drug concentration tests were performed on the Abbott Architect c8000, other hormones/markers on the i2000 of the same brand. Results: Factors that increased the measurement uncertainty of a test were due to external quality control data. The calculations showed that 13 of 26 parameters satisfied quality requirements. The highest uncertainty value, with 28% belonged to cancer antigen 19-9 test. The lowest value was calculated for prolactin with 8.3%. Dehydroepiandrosterone sulfate and phenytoin performed poorly in terms of measurement uncertainty, although internal and external quality control assessment results were considered favourable for both. Conclusion: It is recommended that the concept of measurement uncertainty, which plays an important role in the total quality performance of the laboratory, should be followed up by the clinical laboratory experts at certain time intervals and should be increased the awareness of clinicians about the subject.

Indexed as

LaboratoriesProlactinHumansImmunoassayQuality ControlUncertaintyProlactinimmunoassayquality controluncertainty

Identifiers

PMID35966261
PMCPMC9344870

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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.