Evidence map›Paper›PMID 25588371›Full record

ArticleRheumatology international2015

Relationship between cigarette smoking and radiographic knee osteoarthritis in Chinese population: a cross-sectional study.

Yi Zhang, Chao Zeng, Hui Li, Tuo Yang, Zhen-Han Deng, Ye Yang, Xiang Ding, Dong-Xing Xie, Yi-Lun Wang, Guang-Hua Lei

Abstract read
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In one paragraph

Article in Rheumatology international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 pooled it
5.9field-weighted citation impact, top 4% 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

25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 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

10 authors at 2 institutions in 1 country.

Yi ZhangDepartment of Orthopaedics, Xiangya Hospital, Central South University, #87 Xiangya Road, Changsha, 410008, Hunan Province, China.
Chao Zeng
Hui Li
Tuo Yang
Zhen-Han Deng
Ye Yang
Xiang Ding
Dong-Xing Xie
Yi-Lun Wang
Guang-Hua Lei
Central South University · CNXiangya Hospital Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this paper was to estimate the cross-sectional association between cigarette smoking and radiographic knee Osteoarthritis (OA) in Chinese population. A total of 3,789 subjects (1,796 females and 1,993 males) participated in this study. A subject was diagnosed with radiographic knee OA if Kellgren-Lawrence (K-L) grade ≥2 in at least one leg. The smoking status was classified into four levels based on the daily smoking habit: (1) 0/day; (2) 1-10/day; (3) 11-20/day; and (4) >20/day. Linear trend and multivariable logistic regression were conducted for statistical analysis. The prevalence of radiographic knee OA was 28.4 % among the subjects of this study. An inverse association was observed between cigarette smoking and radiographic knee OA in the linear trend test. Such association remained valid after adjusting the factors of age, gender, body mass index, betel quilt chewing status, physical activity, alcohol drinking status, mean total energy intake and educational level in the multivariable logistic regression. This study suggested a negative association between cigarette smoking and radiographic knee OA in the Chinese population. The findings of this study need to be confirmed by further prospective research.

Indexed as

Asian PeopleChinaChi-Square DistributionCross-Sectional StudiesFemaleHumansKnee JointLinear ModelsLogistic ModelsMaleMiddle AgedMultivariate AnalysisOsteoarthritis, KneePrevalenceRadiographyRisk Assessment

Identifiers

PMID25588371
OpenAlexW2027755665

What OpenQuestion holds

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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.