Evidence map›Paper›PMID 32770980›Full record

ArticleBMC cancer2020

Education level as a predictor of survival in patients with multiple myeloma.

Limei Xu, Xiuju Wang, Xueyi Pan, Xiaotao Wang, Qing Wang, Bingyi Wu, Jiahui Cai, Ying Zhao, Lijuan Chen, Wuping Li and 1 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.0field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 27 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

11 authors at 9 institutions in 1 country.

Limei XuDepartment of Hematology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Xiuju WangDepartment of Hematology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
Xueyi PanDepartment of Hematology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China.
Xiaotao WangDepartment of Hematology, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
Qing WangDepartment of Hematology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Bingyi WuDepartment of Hematology, Shunde Hospital of Southern Medical University, Shunde, Guangdong, China.
Jiahui CaiDepartment of Hematology, Shunde Hospital of Southern Medical University, Shunde, Guangdong, China.
Ying ZhaoDepartment of Hematology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Lijuan ChenDepartment of Hematology, The First Affiliated Hospital With Nanjing Medical University, Nanjing, Jiangsu, China.
Wuping LiDepartment of Internal Medicine, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China.
Juan LiDepartment of Hematology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China. juanlihematology@163.com.
Southern Medical University · CNThe First Affiliated Hospital, Sun Yat-sen University · CNFirst Affiliated Hospital of Guangdong Pharmaceutical University · CNFirst People's Hospital of Foshan · CNGuilin Medical University · CNGuizhou Provincial People's Hospital · CNJiangxi Provincial Cancer Hospital · CNNanjing Medical University · CNSun Yat-sen Memorial Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDisparities in multiple myeloma (MM) prognosis based on sociodemographic factors may exist. We investigated whether education level at diagnosis influenced Chinese MM patient outcomes.

methodsWe performed a multicenter retrospective analysis of data from 773 MM patients across 9 centers in China from 2006 to 2019. Sociodemographic and clinical factors at diagnosis and treatment regimens were recorded, and univariate and multivariate analyses were performed.

resultsOverall, 69.2% of patients had low education levels. Patients with low education levels differed from those with high education levels in that they were more likely to be older, and a higher proportion lived in rural areas, were unemployed, had lower annual incomes and lacked insurance. Additionally, compared to patients with high education levels, patients with low education levels had a higher proportion of international staging system (ISS) stage III classification and elevated lactate dehydrogenase (LDH) levels and underwent transplantation less often. Patients with high education levels had a median progression-free survival (PFS) of 67.50 (95% confidence interval (CI): 51.66-83.39) months, which was better than that of patients with low education levels (30.60 months, 95% CI: 27.38-33.82, p < 0.001). Similarly, patients with high education levels had a median overall survival (OS) of 122.27 (95% CI: 117.05-127.49) months, which was also better than that of patients with low education levels (58.83 months, 95% CI: 48.87-62.79, p < 0.001). In the multivariable analysis, patients with high education levels had lower relapse rates and higher survival rates than did those with low education level in terms of PFS and OS (hazard ratio (HR) = 0.50 [95% CI: 0.34-0.72], p < 0.001; HR = 0.32 [0.19-0.56], p < 0.001, respectively).

conclusionsLow education levels may independently predict poor survival in MM patients in China.

Indexed as

Educational StatusAge FactorsAnalysis of VarianceChinaConfidence IntervalsFemaleHumansIncomeL-Lactate DehydrogenaseMaleMedically UninsuredMiddle AgedMultiple MyelomaProgression-Free SurvivalRetrospective StudiesRural PopulationL-Lactate DehydrogenaseEducation levelMultiple myelomaSociodemographic statusSurvival prognosis

Identifiers

PMID32770980
PMCPMC7414648
OpenAlexW3047947652

What OpenQuestion holds

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LicenceCC BY
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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.