Evidence map›Paper›PMID 41157488›Full record

ArticleSensors (Basel, Switzerland)2025

CrowdAttention: An Attention Based Framework to Classify Crowdsourced Data in Medical Scenarios.

Julian Gil-Gonzalez, David Cárdenas-Peña, Álvaro A Orozco, German Castellanos-Dominguez, Andrés Marino Álvarez-Meza

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

5 authors.

Julian Gil-GonzalezAutomatics Research Group, Universidad Tencológica de Pereira, Pereira 660003, Colombia.ORCID 0000-0002-7405-6379
David Cárdenas-PeñaAutomatics Research Group, Universidad Tencológica de Pereira, Pereira 660003, Colombia.ORCID 0000-0002-0522-8683
Álvaro A OrozcoAutomatics Research Group, Universidad Tencológica de Pereira, Pereira 660003, Colombia.
German Castellanos-DominguezSignal Processing and Recognition Group, Universidad Nacional de Colombia, Sede Manizales, Manizales 170003, Colombia.ORCID 0000-0002-0138-5489
Andrés Marino Álvarez-MezaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Sede Manizales, Manizales 170003, Colombia.ORCID 0000-0003-0308-9576

Funding

Ministerio de Ciencia, Tecnología e Innovación 111091991908
6 · The paper itself

Abstract

Supervised learning models in healthcare and other domains heavily depend on high-quality, labeled data. However, acquiring expert-verified labels (i.e., the gold standard) is often impractical due to cost, time, and subjectivity. Crowdsourcing offers a scalable alternative by collecting labels from multiple non-expert annotators; however, it introduces label noise due to the heterogeneity of annotators. In this work, we propose

Indexed as

CrowdsourcingAlgorithmsDeep LearningHumansNeural Networks, Computerattention mechanismsclassificationdeep learninglearning from crowds

Identifiers

PMID41157488
PMCPMC12567838

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.