ReviewNPJ precision oncology2026
A unified framework for pre-screening and screening tools in oncology clinical trials.
Review in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- Early Identification and Prognostic Stratification of Cancer Cachexia Using Explainable Machine Learning: A Multicentre Cohort Study.Journal of cachexia, sarcopenia and muscle · 2026Observational
- Digital Tools and Strategies for Engaging Patients in Cancer Clinical Trials.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical trial enrollment in oncology remains limited by increasingly complex eligibility criteria, biomarker stratification, and fragmented clinical data, contributing to prolonged recruitment timelines and low participation rates. This review examines contemporary pre-screening and screening approaches, spanning manual workflows, health-system-embedded digital tools, and emerging artificial intelligence-enabled methods. We assess the relative strengths and limitations of large language model-based strategies, including retrieval-augmented and domain-adapted approaches, in addressing scalability, accuracy, and equity challenges. Hybrid frameworks that integrate automated screening with clinician oversight appear most effective in improving trial matching efficiency, representativeness, and timely access to investigational therapies across diverse oncology populations.
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.