Evidence map›Paper›PMID 23680237›Full record

ReviewBMC medicine2013

Personalized medicine in psychiatry: problems and promises.

Uzoezi Ozomaro, Claes Wahlestedt, Charles B Nemeroff

Abstract readReview
In one paragraph

Review in BMC medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
87citing papers in PubMed, 5 pooled it
–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

87 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  5. Personalized (N-of-1) Trials for Depression: A Systematic Review.Journal of clinical psychopharmacology · 2018
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27 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

3 authors.

Uzoezi OzomaroUniversity of Miami, Leonard M, Miller School of Medicine, Miami, FL, USA.
Claes Wahlestedt
Charles B Nemeroff

Funding

Comprehensive analysis of the FMR1 locus transcriptional landscapeR01MH084880 · NIMH · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI WAHLESTEDT, CLAES ROBERT · 2010 to 2014
$2.0M
Role of noncoding RNAs in schizophreniaR01MH083733 · NIMH · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI WAHLESTEDT, CLAES ROBERT · 2008 to 2012
$2.0M
1 of 2 - Prospective Determination of Psychobiological Risk Factors for PTSDR01MH094759 · NIMH · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI NEMEROFF, CHARLES B · 2012 to 2016
$1.8M
NIMH NIH HHS F31 MH862752NIMH NIH HHS MH083733NIMH NIH HHS MH084880NIMH NIH HHS MH094759
6 · The paper itself

Abstract

The central theme of personalized medicine is the premise that an individual's unique physiologic characteristics play a significant role in both disease vulnerability and in response to specific therapies. The major goals of personalized medicine are therefore to predict an individual's susceptibility to developing an illness, achieve accurate diagnosis, and optimize the most efficient and favorable response to treatment. The goal of achieving personalized medicine in psychiatry is a laudable one, because its attainment should be associated with a marked reduction in morbidity and mortality. In this review, we summarize an illustrative selection of studies that are laying the foundation towards personalizing medicine in major depressive disorder, bipolar disorder, and schizophrenia. In addition, we present emerging applications that are likely to advance personalized medicine in psychiatry, with an emphasis on novel biomarkers and neuroimaging.

Indexed as

BiomarkersEpigenesis, GeneticForecastingGenetic Predisposition to DiseaseHumansMental DisordersNeuroimagingPrecision MedicinePsychiatryBiomarkers

Identifiers

PMID23680237
PMCPMC3668172

What OpenQuestion 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 OpenQuestion graph.