Evidence map›Paper›PMID 38550951›Full record

ReviewCambridge prisms. Precision medicine2023

Maturing pharmacogenomic factors deliver improvements and cost efficiencies.

Joseph P Jarvis, Scott E Megill, Peter Silvester, Jeffrey A Shaman

Abstract readReview
In one paragraph

Review in Cambridge prisms. Precision medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. IntroducingCambridge prisms. Precision medicine · 2023
    Article
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

4 authors.

Joseph P JarvisCoriell Life Sciences, Philadelphia, PA, USA.
Scott E MegillCoriell Life Sciences, Philadelphia, PA, USA.
Peter SilvesterThermo Fisher Scientific, Waltham, MA, USA.
Jeffrey A ShamanCoriell Life Sciences, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0001-7397-5755

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An ever-expanding annotation of the human genome sequence continues to promise a new era of precision medicine. Advances in knowledge management and the ability to leverage genetic information to make clinically relevant, predictive, diagnostic, and targeted therapeutic choices offer the ability to improve patient outcomes and reduce the overall cost of healthcare. However, numerous barriers have resulted in a modest start to the clinical use of genetics at scale. Examples of successful deployments include oncologic disease treatment with targeted prescribing; however, even in these cases, genome-informed decision-making has yet to achieve standard of care in most major healthcare systems. In the last two decades, advances in genetic testing, therapeutic coverage, and clinical decision support have resulted in early-stage adoption of pharmacogenomics - the use of genetic information to routinely determine the safety and efficacy profile of specific medications for individuals. Here, through their complicated histories, we review the current state of pharmacogenomic testing technologies, the information tools that can unlock clinical utility, and value-driving implementation strategies that represent the future of pharmacogenomics-enabled healthcare decision-making. We conclude with real-world economic and clinical outcomes from a full-scale deployment and ultimately provide insight into potential tipping points for global adoption, including recent lessons from the rapid scale-up of high-volume test delivery during the global SARS-CoV2 epidemic.

Indexed as

adverse drug reactionsmedication managementmedication safetypersonalized medicinepharmacogeneticspolypharmacy

Identifiers

PMID38550951
PMCPMC10953741

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.