Evidence map›Paper›PMID 16285839›Full record

ArticlePLoS medicine2005

Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature.

Zhenglun Pan, Thomas A Trikalinos, Fotini K Kavvoura, Joseph Lau, John P A Ioannidis

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS medicine, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 41 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 41 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

71 citing papers in PubMed, 41 syntheses or guidelines pooled it.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Zhenglun PanDepartment of Rheumatology, Shandong Provincial Hospital, Jinan 250021, Shandong, China.
Thomas A Trikalinos
Fotini K Kavvoura
Joseph Lau
John P A Ioannidis

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. METHODS AND

findingsWe targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14-35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2-21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se).

conclusionOur data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general.

Indexed as

BiasEpidemiologic StudiesGenetic Predisposition to DiseaseAsian PeopleChinaHumansLanguagePublishing

Identifiers

PMID16285839
PMCPMC1285066

What OpenQuestion holds

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Registered trials

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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.