Suggestions to the article: demonstrating the ascendancy of COVID-19 research using acronyms.

Julie Chi Chow, Tsair-Wei Chien, Willy Chou

Abstract read

In one paragraph

Article in Scientometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it

0cells of the map it votes in

12citing 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

12 citing papers in PubMed.

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.

Julie Chi ChowDepartment of Paediatrics, Chi Mei Medical Center, Tainan, 700 Taiwan.

Tsair-Wei ChienMedical Research Department, Chi-Mei Medical Center, Tainan, Taiwan.

Willy ChouDepartment of Physical Medicine and Rehabilitation, Jiali Chi-Mei Hospital, Chi-Mei Medical Center, 901 Chung Hwa Road, Yung Kung Dist., Tainan, 710 Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The article published on 16 May 2021 is interesting and impressive, particularly on the Figure displaying several acronyms in trend. Although the most popular eight acronyms in 2019 and 2020 are individually highlighted and labeled, how to determine the points in 2019 and 2020 is required for classifications. The analysis for the evolution of keywords is common and necessary in the bibliographic study. None of the studies addressed the determination of the bursting point for a given keyword over the years. We aim to illustrate the way to determine the inflection point on a given ogive curve and apply the temporal bar graph (TBG) to interpret the trend of a specific keyword (or acronym). The prediction model is based on item response theory, commonly used in educational and psychometric fields. The eight acronyms presented in the previous study were demonstrated using the TBG. We found that the TBG includes more valuable information than the traditional trend charts. The inflection point denoted the topic burst indicates the turning point suddenly from increasing to decreasing. The TBG combined with the inflection point to represent the trend of a given keyword can make the data in trend easier and clearer to understand than any graph used in ever before bibliometric analyses.

Indexed as

AcronymBibliometric analysisItem response theoryPrediction modelTemporal bar graph

Identifiers

PMID35309245

DOI10.1007/s11192-022-04302-z

PMCPMC8916907

What OpenQuestion holds

Textmetadata

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.