Evidence map›Paper›PMID 41605495›Full record

ArticleJMIR formative research2026

Development and Validation of a Protein Electrophoresis Classification Algorithm: Tabular Data-Based Alternative.

Auriane Mazuir, Gatien Ricotier, Pierre Filhine-Tresarrieu

Abstract read
In one paragraph

Article in JMIR formative research, 2026. 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

3 authors.

Auriane Mazuir *Laboratoire B2A, Brumath, France.ORCID 0009-0007-0476-2642
Gatien Ricotier *Laboratoire B2A, Brumath, France.ORCID 0009-0007-9974-231X
Pierre Filhine-Tresarrieu *Laboratoire B2A, Brumath, France.ORCID 0009-0000-7103-7386

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Serum protein electrophoresis (SPE) is routinely interpreted through visual assessment of electropherogram images by medical laboratory scientists. We introduce an efficient tabular data-based machine learning approach that directly leverages numerical SPE profiles, offering a robust and interpretable alternative to image-based deep learning methods.

Indexed as

AlgorithmsBlood Protein ElectrophoresisClassification AlgorithmsHumansMachine LearningCatBoostclinical informaticscomputational efficiencyconvolutional neural networkdiagnostic interpretationmachine learningserum protein electrophoresistabular data analysis

Identifiers

PMID41605495
PMCPMC12895147

What OpenQuestion holds

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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.