Evidence map›Paper›PMID 31800621›Full record

ArticlePloS one2019

Interactions of pharmaceutical companies with world countries, cancers and rare diseases from Wikipedia network analysis.

Guillaume Rollin, José Lages, Tatiana S Serebriyskaya, Dima L Shepelyansky

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Guillaume RollinInstitut UTINAM, CNRS, UMR 6213, OSU THETA, Université de Bourgogne Franche-Comté, Besançon, France.
José LagesInstitut UTINAM, CNRS, UMR 6213, OSU THETA, Université de Bourgogne Franche-Comté, Besançon, France.ORCID 0000-0001-5965-8876
Tatiana S SerebriyskayaLaboratory for Translational Research and Personalized Medicine, Moscow Institute of Physics and Technology, Moscow, Russia.ORCID 0000-0002-7129-1736
Dima L ShepelyanskyLaboratoire de Physique Théorique, IRSAMC, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France.ORCID 0000-0002-2752-0765

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using the English Wikipedia network of more than 5 million articles we analyze interactions and interlinks between the 34 largest pharmaceutical companies, 195 world countries, 47 rare renal diseases and 37 types of cancer. The recently developed algorithm using a reduced Google matrix (REGOMAX) allows us to take account both of direct Markov transitions between these articles and also of indirect transitions generated by the pathways between them via the global Wikipedia network. This approach therefore provides a compact description of interactions between these articles that allows us to determine the friendship networks between them, as well as the PageRank sensitivity of countries to pharmaceutical companies and rare renal diseases. We also show that the top pharmaceutical companies in terms of their Wikipedia PageRank are not those with the highest market capitalization.

Indexed as

Drug IndustryInternationalityInternetAlgorithmsHumansMarketingNeoplasmsRare DiseasesStatistics, Nonparametric

Identifiers

PMID31800621
PMCPMC6892506

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.