ReviewThe AAPS journal2026
Best practices in the application of parallelism for biomarker assay validation.
Review in The AAPS journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Quantification of blood-based neurovascular unit biomarkers: an analytical platform comparison study.Resuscitation plus · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An assessment of parallelism is critical for biomarker assays to confirm whether the assay recognizes the endogenous analyte similarly to the calibrator, the suitability of a surrogate calibrator matrix and the potential need for a minimal required dilution. While the importance of parallelism has been raised in numerous publications there remains a lack of detail on how to conduct and interpret parallelism experiments, as well as some confusion between parallelism, dilution linearity, and spike recovery experiments. This best practice paper provides a detailed discussion of the reasons for conducting parallelism, as well as recommendations for when to conduct parallelism experiments, the number of samples needed, the selection of appropriate surrogate matrices, the interpretation of parallelism data, including graphical and statistical methods, and parallelism results reporting. It emphasizes the need for continuous evaluation of parallelism throughout the assay life cycle to ensure reliable measurement of the desired analyte within the context of use. Finally, a number of short case studies are provided to illustrate the application and interpretation of parallelism.
Indexed as
Identifiers
41530473What OpenQuestion holds
Registered trials
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.