Evidence map›Paper›PMID 27473753›Full record

SynthesisChinese journal of cancer2016

Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis.

Ashutosh Kumar Dubey, Umesh Gupta, Sonal Jain

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Chinese journal of cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
12.3field-weighted citation impact, top 1% 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

35 citing papers in PubMed, 99 citations in OpenAlex.

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  17. The relationship between nutritional status and prognosis in patients with locally advanced and advanced stage lung cancer.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021
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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

3 authors at 1 institution in 1 country.

Ashutosh Kumar DubeyInstitute of Engineering and Technology, JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura, Ajmer Road, Jaipur, Rajasthan, 302 026, India. ashutoshdubey123@gmail.com.
Umesh GuptaInstitute of Engineering and Technology, JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura, Ajmer Road, Jaipur, Rajasthan, 302 026, India.
Sonal JainInstitute of Engineering and Technology, JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura, Ajmer Road, Jaipur, Rajasthan, 302 026, India.
JK Lakshmipat University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOwing to the use of tobacco and the consumption of alcohol and adulterated food, worldwide cancer incidence is increasing at an alarming and frightening rate. Since the last decade of the twentieth century, lung cancer has been the most common cancer type. This study aimed to determine the global status of lung cancer and to evaluate the use of computational methods in the early detection of lung cancer.

methodsWe used lung cancer data from the United Kingdom (UK), the United States (US), India, and Egypt. For statistical analysis, we used incidence and mortality as well as survival rates to better understand the critical state of lung cancer.

resultsIn the UK and the US, we found a significant decrease in lung cancer mortalities in the period of 1990-2014, whereas, in India and Egypt, such a decrease was not much promising. Additionally, we observed that, in the UK and the US, the survival rates of women with lung cancer were higher than those of men. We observed that the data mining and evolutionary algorithms were efficient in lung cancer detection.

conclusionsOur findings provide an inclusive understanding of the incidences, mortalities, and survival rates of lung cancer in the UK, the US, India, and Egypt. The combined use of data mining and evolutionary algorithm can be efficient in lung cancer detection.

Indexed as

Early Detection of CancerEgyptFemaleHumansIncidenceIndiaLung NeoplasmsMaleUnited KingdomUnited StatesData miningEvolutionary algorithmsIncidence and mortality ratesLung cancer

Identifiers

PMID27473753
PMCPMC4967338
OpenAlexW2503261857

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.