Evidence map›Paper›PMID 10658556›Full record

ArticleOccupational and environmental medicine1999

Applications of new technology in molecular epidemiology and their relevance to occupational medicine.

D Koh, A Seow, C N Ong

Abstract read
In one paragraph

Article in Occupational and environmental medicine, 1999. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Surveillance in occupational health.Occupational and environmental medicine · 2003
    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

3 authors.

D KohDepartment of Community, Occupational and Family Medicine, Faculty of Medicine, National University of Singapore, Republic of Singapore. cofkohd@nus.edu.sg
A Seow
C N Ong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technological advances in molecular biology over the past 2 decades have offered more complex techniques that can be used to study the role of specific exogenous agents and host variables that cause ill health. Increasingly, studies in human populations use this new technology, combined with epidemiological methods, to shed light on the understanding of the biological processes associated with development of disease. This approach has many potential applications in occupational and environmental medicine (OEM), and some aspects of the work in this growing field are reviewed. An understanding of biochemistry and genetics at the molecular level, specific knowledge on metabolism and mechanisms of action, and epidemiology have become increasingly important for the OEM practitioner. This is necessary to consider the major question of validation and relevance of these molecular biomarkers. As end users, OEM practitioners should also consider the impact of these advances on their practices. For example, the availability of genetic tests to identify susceptible workers raises issues of ethics, individual privacy, right to work, and the relevance of such tests. Several studies have presented data on the association of environmental measurements and various biomarkers for internal and biologically effective dose, genetic polymorphisms, and early response markers. Given the limitations of individual molecular biomarkers in assessing risk to health, and the multifactorial nature of environmental disease, it is likely that such an approach will increase our understanding of the complex issue of mechanisms of disease and further refine the process of risk assessment.

Indexed as

Genetic MarkersGenetic VariationHumansMolecular EpidemiologyOccupational MedicineSingaporeGenetic Markers

Identifiers

PMID10658556
PMCPMC1757685

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.