Evidence map›Paper›PMID 36828499›Full record

ArticleTropical medicine and infectious disease2023

A Practical Approach in Refining Binary Outcome for Treatment Effect of COVID-19 According to Geographical Diversity.

I-Shiang Tzeng

Open access · goldAbstract read
In one paragraph

Article in Tropical medicine and infectious disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 3 pooled it
0.8field-weighted citation impact, top 32% 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

4 citing papers in PubMed, 3 syntheses or guidelines pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. 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

1 author at 1 institution in 1 country.

I-Shiang TzengDepartment of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan.ORCID 0000-0002-9047-8141
Taipei Tzu Chi Hospital · TW

Funding

Taipei Tzu Chi Hospital TCRD-TPE-111-08(2/2)
6 · The paper itself

Abstract

The recent COVID-19 pandemic has drawn attention to health and economics worldwide. Initially, diseases only ravage local populations, while a pandemic could aggravate global economic burdens. Lopinavir/Ritonavir is an anti-HIV drug that was used on small scale patients during SARS, but its effectiveness for COVID-19 treatment is still unclear. Previous studies or meta-analysis have retrieved clinical data of subgroup analysis to evaluate the efficacy and safety of Lopinavir/Ritonavir for the treatment of COVID-19 in a few affected regions. However, geographical diversity and small number of studies bias correction were not achieved in such subgroup analysis of published meta-analysis. The present study demonstrates a practical approach in refining the binary outcome for COVID-19 treatment of Lopinavir/Ritonavir according to geographical location diversity and small number of studies (less than or equal to five) for subgroup analysis. After performing practical approach, the risk of adverse event with LPV/RTV for treatment of COVID-19 becomes nonsignificant compared to previous meta-analysis. Furthermore, we also notice heterogeneity of random effect of meta-analysis may be declined after proposed adjustment. In conclusion, proposed practical approach is recommend for performing a subgroup analysis to avoid concentration in a single geographical location and small number of studies bias.

Indexed as

COVID-19efficacygeographical diversityLopinavir/Ritonavirmeta-analysissafetysmall number of studies bias

Identifiers

PMID36828499
PMCPMC9964378
OpenAlexW4318215081

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.