Draft:Parallelism (laboratory test)

  • Comment: Most of the sourcing is primary, without review articles, see the comment about the first photo being possibly inappropriate, and WP:MOS also applies. Bluethricecreamman (talk) 15:26, 26 July 2024 (UTC)
  • Comment: the first photo is very suspect. its missing a caption, I'm not sure about its fair usage rights, and its clearly about Serial dilution, which I am not sure how it relates to parallelism Bluethricecreamman (talk) 15:24, 26 July 2024 (UTC)
  • Comment: Please make the first sentence tell the reader what parallelism is. Also don't use "we" or "us" in encyclopedia articles. Graeme Bartlett (talk) 23:20, 10 May 2024 (UTC)

Parallelism (laboratory test)

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Parallelism, in the context of laboratory research refers to the degree to which different samples or assays produce consistent and proportional results, ensuring that comparisons and measurements are accurate and reliable across different conditions or experiments.

Definition of Parallelism for a laboratory test

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This section is about parallelism in a specific type of test done in a laboratory. These tests, called quantitative laboratory tests, figure out how much of a substance is in a sample[1]. For instance, they might measure how much glucose is in a blood sample. No matter the method used, it is mandatory to figure out the unknown concentration in the sample by comparing it to a known standard. This is done mathematically using regression analysis. For the mathematics to be right, the serial dilution curve of the substance in the sample has to match up with the dilution curve of the standard. So, in laboratory tests, parallelism means there is a similar relationship between the substance being measured (like a biomarker) and the signal from the standard.

 

Regulatory authorities

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Regulatory authorities such as the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have made presence of parallelism mandatory for the approval of analytical methods used in medicine. Guidelines have been produced for the approval of bioanalytical methods that indicate how parallelism tests should be evaluated.[2]

Determination of parallelism

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Parallelism of a laboratory test can be determined visually or statistically.[3]. Each method has there advantages and limitations. For people without a statistical background the visual assessment is likely more intuitive[1]

Presence of parallelism

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Presence of parallelism is important for the reliable and accurate reporting of data. In routine laboratory practise parallelism can frequently be demonstrated for at least a partial range of the analyte.[4]. Therefore partial parallelism plots give a quick visual answer. An example is given for presence of parallelism comparing the dilution curve of a reference standard with the dilution curve of several samples. All horizontal lines in the figure are parallel between the red vertical reference lines[3]

 
Presence of parallelism (laboratory test)[4]

Absence of parallelism

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Absence of parallelism is a problem because calculations may not be valid. An example for lack of parallelism is shown. The sample dilution curves are not parallel to the horizontal dilution curve of the reference standard.

 
Absence of paralellism (laboratory test)[4]

References

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  1. ^ a b Plikaytis, B D; Holder, P F; Pais, L B; Maslanka, S E; Gheesling, L L; Carlone, G M (October 1994). "Determination of parallelism and nonparallelism in bioassay dilution curves". Journal of Clinical Microbiology. 32 (10): 2441–2447. doi:10.1128/jcm.32.10.2441-2447.1994. PMC 264081. PMID 7814480.
  2. ^ "7. Additional considerations" (PDF), ICH guideline M10 on bioanalytical method validation and study sample analysis, European Medicines Agency, 25 July 2022
  3. ^ a b Pum, Joachim (2019). "A practical guide to validation and verification of analytical methods in the clinical laboratory". Advances in Clinical Chemistry. 90: 215–281. doi:10.1016/bs.acc.2019.01.006. ISBN 978-0-12-817179-0. PMID 31122610.
  4. ^ a b c Petzold, Axel (January 2024). "Partial Parallelism Plots". Applied Sciences. 14 (2): 602. doi:10.3390/app14020602.