Evidence map›Paper›PMID 32214846›Full record

ArticleCancer management and research2020

Prediction and Monitoring Method for Breast Cancer: A Case Study for Data from the University Hospital Centre of Coimbra.

Jin Yue, Na Zhao, Liu Liu

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

Article in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.8field-weighted citation impact, top 36% 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

0 citing papers in PubMed, 6 citations in OpenAlex.

No citing paper in PubMed yet.

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 3 institutions in 1 country.

Jin Yue *School of Mathematics and VC & VR Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, People's Republic of China.ORCID 0000-0001-5546-9525
Na Zhao *Department of Clinical Laboratory and Guangdong Provincial Key Laboratory of Occupational Disease Prevention and Treatment, Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, People's Republic of China.
Liu LiuSchool of Mathematics and VC & VR Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, People's Republic of China.ORCID 0000-0002-4792-0264
Guangdong Provincial Occupational Disease Prevention Hospital · CNSichuan Normal University · CNSichuan University of Arts and Science · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is the second most common cancer in women after skin cancer. Breast cancer can occur in both men and women, but it is far more common in women. Real-time monitoring of breast cancer indicators is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In this paper, we provide a nonparametric statistical method to predict and detect breast cancer occur. The exponentially weighted moving average (EWMA) control scheme is based on rank methods so that it is completely nonparametric. It is efficient in detecting the shifts for multivariate processes. A real example data from the University Hospital Centre of Coimbra is given to illustrate this method.

Indexed as

breast cancerEWMAnonparametricrank-based method

Identifiers

PMID32214846
PMCPMC7080965
OpenAlexW3011558691

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.