ReviewClinical and experimental medicine2026
Innovations in biomarker stratification for precision oncology.
Review in Clinical and experimental medicine, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomarker stratification underpins precision oncology, yet survival analysis often relies on arbitrary thresholds that undermine reproducibility and clinical relevance, particularly for continuous biomarkers. This review focuses on methodological approaches for stratifying continuous biomarkers within survival analysis frameworks, examining conventional strategies alongside data-driven and machine learning methods in the context of threshold selection and clinical interpretability. We evaluate the extent to which these approaches address key challenges including heterogeneity, confounding, and overfitting, and critically appraise their strengths and limitations for clinically actionable risk stratification. By synthesising current evidence, we highlight opportunities for more robust and reproducible prognostic modelling and outline future directions to improve the reliability of biomarker-driven decision-making in oncology.
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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.