Evidence map›Paper›PMID 33717316›Full record

ArticleAsian bioethics review2019

Precision Medicine and Big Data: The Application of an Ethics Framework for Big Data in Health and Research.

G Owen Schaefer, E Shyong Tai, Shirley Sun

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Asian bioethics review, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
11.0field-weighted citation impact, top 2% 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

13 citing papers in PubMed, 38 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

G Owen SchaeferCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0000-0002-6915-6148
E Shyong TaiSaw Swee Hock School of Public Health, National University of Singapore, Singapore.ORCID 0000-0003-2929-8966
Shirley SunSociology, School of Social Sciences and Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
National University of Singapore · SGNanyang Technological University · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As opposed to a 'one size fits all' approach, precision medicine uses relevant biological (including genetic), medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone's disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in the

Indexed as

Big dataBioethicsGenetic discriminationGenomicsPrecision medicine

Identifiers

PMID33717316
PMCPMC7747352
OpenAlexW2977662652

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.