ArticleScientific reports2026
Reference intervals reimagined with IRIS for earlier detection and better disease monitoring.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Reference intervals (RIs) are the universal established methodology for interpreting numerical clinical data by comparing individual test results to population-based benchmarks. The quality of these RIs significantly influences individual-level decision-making. This study aims to answer the question on how we can effectively leverage an individual's personal data in conjunction with that of their peers to compute individual reference intervals (IRIs) for key clinical parameters relevant to disease detection and progression. We describe the IRIS workflow that includes prior data processing and data quality check procedures. The computation of IRI involves the test results of multiple "healthy" data points from the same subject(s) and also from the peers. The model adjusts for covariates like sex and age, enhancing accuracy. The workflow demonstrated the potential utility of IRI in clinical and omics data from two longitudinal studies. For healthy populations, IRIs showed diagnostic value in chronic diseases, while in diseased cohorts, they enabled effective disease monitoring. An integrated application IRIS has been developed, incorporating all described steps in an easy-to-use tool in research and/or clinical practice. The IRI may assist in (1) early detection of disease transition in chronic diseases and (2) monitoring personal disease progression. It facilitates the detection of small deviations in clinical measurements, either using standard clinical biochemistry test results or omics data. With adequate data infrastructure, the IRIS workflow can be integrated into clinical practice by embedding personalised biomarker baselines into AI-enabled decision support systems Such integration provides a complementary layer that enhances the sensitivity and specificity of clinical alerts and risk stratification. Ultimately, this approach has the potential to transform personalised disease diagnosis, management, and patient outcomes.
Indexed as
Identifiers
What 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.