Evidence map›Paper›PMID 35259407›Full record

SynthesisEnvironmental research2022

Meta-analysis to explain unknown causes of the origins of SARS-COV-2.

Mario Coccia

Open access · greenAbstract readMeta-Analysis
In one paragraph

Synthesis in Environmental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
8.4field-weighted citation impact, top 2% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. 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

1 author at 1 institution in 1 country.

Mario CocciaCNR -- National Research Council of Italy, Collegio Carlo Alberto, Via Real Collegio, N. 30, 10024, Moncalieri, TO, Italy. Electronic address: mario.coccia@cnr.it.
Collegio Carlo Alberto · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

New Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes the Coronavirus Disease 2019 (COVID-19), an infectious illness that has generated a pandemic crisis worldwide. One of the fundamental questions in science and society is how SARS-CoV-2 has been originated to design best practices directed to prevent and/or to cope with future hazardous pathogens. The study confronts this question here developing a meta-analysis, which endeavors to explain, whenever possible, unknown sources of the SARS-CoV-2. Findings suggest that the natural spillover of novel viral agents that generate more than 6.00 M deaths worldwide in about two years (such as, SARS-CoV-2 from February 2020 to March 2022) has a remote probability of occurrence (using an analogy with the probability of natural disasters generating a lot of fatalities), whereas science advances on hazardous viral agents and consequential lab accident have a (higher) probability of occurrence (about 13-20% like in manifold lab accidents). The findings of this meta-analysis suggest the vital role of improving the technical guidelines of biosafety at all levels in laboratories during the development of scientific research of experimental virology on hazardous pathogens to minimize risks of pandemic threats in environment and human society.

Indexed as

COVID-19SARS-CoV-2Containment of BiohazardsHumansLaboratoriesPandemicsBiosecurity risksCoronavirusCOVID-19Emerging infectious diseaseHazardous pathogensLaboratory biosafetyMeta-analysisSARS-CoV-2

Identifiers

PMID35259407
PMCPMC8897286
OpenAlexW4221058712

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.