Evidence map›Paper›PMID 36969956›Full record

ArticleFrontiers in digital health2023

A summary of the ComParE COVID-19 challenges.

Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia and 10 more

Open access · goldFull text read
In one paragraph

Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

1 citing paper in PubMed, 15 citations in OpenAlex.

  1. 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

20 authors at 5 institutions in 2 countries.

Harry CoppockDepartment of Computing, Imperial College London, London, United Kingdom.
Alican AkmanDepartment of Computing, Imperial College London, London, United Kingdom.
Christian BerglerDepartment of Computing, FAU Erlangen-Nürnberg, Erlangen-Nürnberg, Germany.
Maurice GerczukInstitute of Computer Science, Universität Augsburg, Augsburg, Germany.
Chloë BrownDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Jagmohan ChauhanDepartment of Computing, University of Southampton, Southampton, United Kingdom.
Andreas GrammenosDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Apinan HasthanasombatDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Dimitris SpathisDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Tong XiaDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Pietro CicutaDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Jing HanDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Shahin AmiriparianInstitute of Computer Science, Universität Augsburg, Augsburg, Germany.
Alice BairdInstitute of Computer Science, Universität Augsburg, Augsburg, Germany.
Lukas StappenInstitute of Computer Science, Universität Augsburg, Augsburg, Germany.
Sandra OttlInstitute of Computer Science, Universität Augsburg, Augsburg, Germany.
Panagiotis TzirakisDepartment of Computing, Imperial College London, London, United Kingdom.
Anton BatlinerInstitute of Computer Science, Universität Augsburg, Augsburg, Germany.
Cecilia MascoloDepartment of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Björn W SchullerDepartment of Computing, Imperial College London, London, United Kingdom.
University of Cambridge · GBUniversity of Augsburg · DEImperial College London · GBFriedrich-Alexander-Universität Erlangen-Nürnberg · DEUniversity of Southampton · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals' respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).

Indexed as

computer auditionCOVID-19deep learningDigital Healthmachine learning

Identifiers

PMID36969956
PMCPMC10031089
OpenAlexW4323567384

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read34
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.