Evidence map›Paper›PMID 42374762›Full record

ArticleCurrent Alzheimer research2026

Corrigendum to: Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.

Abdelmounim Boudi, Jingfei He, Isselmou Abd El Kader, Xiaotong Liu, Mohamed Mouhafid

Abstract readPublished Erratum
PubMed Publisher
In one paragraph

Article in Current Alzheimer 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

5 · Who and what money

Authors and funding

5 authors.

Abdelmounim BoudiSchool of Electronics Information and Engineering, Hebei University of Technology, Tianjin 300401, China.
Jingfei HeSchool of Electronics Information and Engineering, Hebei University of Technology, Tianjin 300401, China.
Isselmou Abd El KaderSchool of International Education, Hebei University of Technology, Tianjin 300401, China.
Xiaotong LiuSchool of Electronics Information and Engineering, Hebei University of Technology, Tianjin 300401, China.
Mohamed MouhafidSchool of Electronics Information and Engineering, Hebei University of Technology, Tianjin 300401, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The publisher identified that references 42 and 43 were duplicated in the published version of this article [1]. The error has now been corrected. The original article can be found online at: https://www.benthamscience.com/article/150567 Details of the error and the correction are provided below: Original: [42] Muksimova S, Umirzakova S, Iskhakova N, Khaitov A, Cho YI. Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI. Comput Biol Med 2025; 190, 110095. [43] Muksimova S, Umirzakova S, Iskhakova N, Khaitov A, Cho YI. Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI. Comput Biol Med 2025; 190: 110095. http://dx.doi.org/10.1016/j.compbiomed.2025.110095 PMID: 40158456 Corrected: [42] Muksimova S, Umirzakova S, Iskhakova N, Khaitov A, Cho YI. Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI. Comput Biol Med 2025; 190, 110095. http://dx.doi.org/10.1016/j.compbiomed.2025.110095 PMID: 40158456 [43] Olaimat MA, Bozdag S, Saeed F. TA-RNN: An attention-based time-aware recurrent neural network architecture to predict progression of Alzheimer's disease. Alzheimer's Dement 2024; 20(S1).

Identifiers

PMID42374762

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.