Evidence mapPaperPMID 24580858Full record

ReviewBMC medicine2014

Personalized medicine: risk prediction, targeted therapies and mobile health technology.

Daniel F Hayes, Hugh S Markus, R David Leslie, Eric J Topol

Abstract readReview
In one paragraph

Review in BMC medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Trial
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  6. Review
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  10. Aging precisely: Precision medicine through the lens of an older adult.Journal of the American Geriatrics Society · 2024
    Review
  11. Review
  12. Review
  13. Bone turnover markers can predict healing time in medication-related osteonecrosis of the jaw.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021
    Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
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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.

Daniel F Hayes
Hugh S Markus
R David Leslie
Eric J Topol

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Personalized medicine is increasingly being employed across many areas of clinical practice, as genes associated with specific diseases are discovered and targeted therapies are developed. Mobile apps are also beginning to be used in medicine with the aim of providing a personalized approach to disease management. In some areas of medicine, patient-tailored risk prediction and treatment are applied routinely in the clinic, whereas in other fields, more work is required to translate scientific advances into individualized treatment. In this forum article, we asked specialists in oncology, neurology, endocrinology and mobile health technology to discuss where we are in terms of personalized medicine, and address their visions for the future and the challenges that remain in their respective fields.

Indexed as

Cell PhoneHumansPharmacogeneticsPrecision MedicinePredictive Value of TestsRisk FactorsTelemedicine

Identifiers

PMID24580858
PMCPMC3938085

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.