ArticleBioanalysis2026
Cut point validation vs. real-world performance: the ultra-low cut point dilemma.
Article in Bioanalysis, 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
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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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cut point determination is a critical component in the development and validation of antidrug antibody (ADA) assays, directly influencing the assessment of immunogenic responses. Removal of analytical and biological outliers from employing Tukey's outlier test may lead to reduced variability and consequently low or ultra-low cut points (ULCPs, defined here as signal-to-noise ratios ≤1.10). Such ULCPs raise concerns about assay reliability and the potential for elevated false-positive rates. To evaluate this, we analyzed ADA studies from two contract research organizations employing ULCPs. Given the heightened assay sensitivity from modern platforms and reagents, our findings indicate that ULCPs do not inherently compromise ADA assay performance or reliability. These results support the continued use of ULCPs when appropriately validated and statistically justified.
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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.