Evidence map›Paper›PMID 35629136›Full record

ArticleJournal of personalized medicine2022

An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology.

Patrick Silva, Deborah Vollmer Dahlke, Matthew Lee Smith, Wendy Charles, Jorge Gomez, Marcia G Ory, Kenneth S Ramos

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Public engagement with genomics.Wellcome open research · 2023
    Review
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Patrick SilvaHealth Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA.ORCID 0000-0002-8870-6440
Deborah Vollmer DahlkeSchool of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA.
Matthew Lee SmithSchool of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA.ORCID 0000-0002-8232-9285
Wendy CharlesBurstIQ, 9635 Maroon Circle, #310, Englewood, CO 80112, USA.ORCID 0000-0003-1627-6899
Jorge GomezHealth Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA.
Marcia G OrySchool of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA.
Kenneth S RamosHealth Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.

Indexed as

cancerchronic diseaseelectronic medical recordgenomichealth disparityregistry

Identifiers

PMID35629136
PMCPMC9144063

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.