Evidence map›Paper›PMID 34934418›Full record

ArticleRevista panamericana de salud publica = Pan American journal of public health2021

[Risk of Publication Bias in Therapeutic Interventions for COVID-19Risco de viés de publicação em intervenções terapêuticas para a COVID-19].

Santiago Hasdeu, Fernando Tortosa

Abstract readEnglish Abstract
In one paragraph

Article in Revista panamericana de salud publica = Pan American journal of public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Santiago HasdeuRed Argentina Pública de Evaluación de Tecnologías Sanitarias Argentina.
Fernando TortosaMinisterio de Salud de la Provincia de Río Negro Argentina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article describes publication bias, its most frequent causes, its characteristics, the regulatory tools to avoid it, and some statistical techniques to analyze it. These techniques are explained and applied to three therapeutic interventions related to the 2019 coronavirus disease (COVID-19): corticosteroids, ivermectin, and tocilizumab. Risk of publication bias was detected for ivermectin and tocilizumab. Systematic reviews and meta-analyses are secondary research designs that provide a reference to guide decision-making. They are prone to different types of bias, i.e., a systematic deviation in the results. Even if carried out with methodological rigor, their validity can be threatened by publication bias. This is defined as the act of concealing or delaying publication, withholding data arising from research studies, or both. Up to half of controlled trials remain unpublished. During the H1N1 virus pandemic, publication bias from industry-funded studies led to the recommendation and large-scale procurement of oseltamivir, a drug that later proved to have no relevant beneficial effects. Two-thirds of clinical trial funding for COVID-19 comes from the pharmaceutical industry. In the context of the COVID-19 pandemic, studies are published at an accelerated pace, making it very important to understand and identify publication bias. To reduce publication bias it is necessary to regulate the registration and publication of clinical trials, but this requires coordination among countries and international bodies. It is important to suspect and attempt to identify publication bias for decision making.

Indexed as

COVID-19Meta-analysis as topicpublication biassystematic reviewtechnology assessment, biomedical

Identifiers

PMID34934418
PMCPMC8678098

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.