ArticleAmerican journal of translational research2021
Modeling factors critical for implementation of precision medicine at health systems-level: an IRT approach.
Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Implementation of precision medicine in treating non-communicable diseases: a systematic review.Journal of translational medicine · 2025Pooled it
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThrough recent advances in omics technologies, precision medicine (PM) promises to fundamentally change the way we approach health, disease and illness. Imperative applications of omics-based biomarkers are gradually moving from research to clinical settings, with huge long-term clinical and public health implications. Whereas much of research in PM is mainly focused on basic biomedical discoveries, currently there is little research on the clinical implementation of omics biomarkers, especially at health systems level. AIM AND
methodsThis study investigated the application of multidimensional item response theory (IRT) models to validate a hypothesized PM implementation measurement model. This is a contribution to PM implementation at health systems level. Data obtained through an item-sort procedure involving 496 observations from 124 study participants formed the basis of a 22-item PMI measurement model.
conclusionStatistical significance of the bifactor model suggests PM implementation may have to be examined using factors that reflect a single common underlying implementation construct, as well as factors that reflect unique variances for the identified four content-specific factors.
Indexed as
Identifiers
34956473PMC8661177What 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.