Evidence map›Paper›PMID 28685051›Full record

ArticleBiomedical reports2017

Personalized medicine could transform healthcare.

Sunil Mathur, Joseph Sutton

Open access · bronzeAbstract read
In one paragraph

Article in Biomedical reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
144citing papers in PubMed, 2 pooled it
5.0field-weighted citation impact, top 3% of its field
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

144 citing papers in PubMed, 2 syntheses or guidelines pooled it, 358 citations in OpenAlex.

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84 more citing papers are in PubMed but not listed here.

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

2 authors at 1 institution in 1 country.

Sunil MathurMolecular Drug Research Laboratory, Edinburgh Napier University, Edinburgh EH11 4BN, UK.
Joseph SuttonMolecular Drug Research Laboratory, Edinburgh Napier University, Edinburgh EH11 4BN, UK.
Edinburgh Napier University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Personalized medicine (PM) is about tailoring a treatment as individualized as the disease. The approach relies on identifying genetic, epigenomic, and clinical information that allows the breakthroughs in our understanding of how a person's unique genomic portfolio makes them vulnerable to certain diseases. PM approach is a complete extension of traditional approach (One-Size-Fits-All) to increasing our ability to predict which medical treatments will be safe and effective for individual patient, and which ones will not be, based on the patient's unique genetic profile. Implementation of PM has the potential to reduce financial and time expenditure, and increase quality of life and life extension of patients. Knowledge of PM facilitates earlier disease detection via enhanced use of existing biomarkers and detection of early genomic and epigenomic events in disease development, particularly carcinogenesis. The PM approach predominantly focuses on preventative medicine and favours taking pro-active actions rather than just reactive. This approach delays or prevents the need to apply more severe treatments which are usually less tolerated and with increased quality of life and financial considerations. Increasing healthcare costs have placed additional pressure on government funded healthcare systems globally, especially regarding end of life care. PM may increase the effectiveness of existing treatments and negate the inherent problems associated with non-PM approaches. PM is a young but rapidly expanding field of healthcare where a physician can select a treatment based on a patient's genetic profile that may not only minimize harmful side effects and guarantee a more successful result, but can be less cost effective compared with a 'trial-and-error' approach to disease treatment. The less efficient non-PM ('trial-and-error') approach, which can lead to drug toxicity, severe side effects, reactive treatment and misdiagnosis continue to contribute to increasing healthcare costs. Increased patient stratification will allow for the enhanced application of PM and pro-active treatment regimens, resulting in reduced costs and quality of life enhancement.

Indexed as

personalized medicinetailored therapytarget therapy

Identifiers

PMID28685051
PMCPMC5492710
OpenAlexW2620804920

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.