Evidence map›Paper›PMID 33537480›Full record

ArticleHeliyon2021

A bibliometric analysis of top 50-most cited articles on repetitive trans-cranial magnetic stimulation (rTMS) for treatment of depression.

Syeda Beenish Bareeqa, Syed Ijlal Ahmed, Syeda Sana Samar, Arsalan Anwar, Mustafa M Husain

Abstract read
In one paragraph

Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

5 authors.

Syeda Beenish BareeqaJinnah Medical and Dental College, Karachi, Pakistan.
Syed Ijlal AhmedLiaquat National Medical College and Hospital, Karachi, Pakistan.
Syeda Sana SamarJinnah Sindh Medical University, Karachi, Pakistan.
Arsalan AnwarDepartment of Medicine, Dow Medical College, Karachi, Pakistan.
Mustafa M HusainDepartment of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCitation count can be used as a key tool to assess the quality of the published literature and because of its immense advantages it is now widely used in ranking the articles on specific topics. OBJECTIVE/HYPOTHESIS: To extract and assess the top cited work on repetitive transcranial magnetic stimulation (rTMS) for depression treatment.

methodsScopus Library Database was searched and two independent authors produced a list of 50 most cited articles on repetitive transcranial magnetic stimulation (rTMS) for treatment of depression. All the relevant articles having key-terms within their titles, abstract and keywords were included in our search. Our list was categorized into two categories, "mixed" and "focused".

resultsThe articles in the produced list of top 50 most cited articles on rTMS for treatment of depression belong to the time period 1993-2012 with total citation count 12078. George MS was prominent in the list. ' LIMITATIONS: Articles that were published before 1993 and after 2012 on rTMS for depression couldn't made it to the final list of top-50 most cited article.

conclusionWe attempted to conduct a topic-specific citation analysis considering the paucity of specified bibliometrics in medical literature. Our research provides an insight on emerging trends in rTMS for depression and highlights the characteristics, quality and dynamics of frequently cited articles in the field.

Indexed as

Bibliometric analysisCitation countDepressionRepetitive transcranial magnetic stimulationTreatment

Identifiers

PMID33537480
PMCPMC7841314

What OpenQuestion holds

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LicenceCC BY
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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.