Evidence map›Paper›PMID 41801307›Full record

SynthesisClinical neuroradiology2026

Diagnostic Accuracy of Machine Learning Models in Predicting Functional Outcome of Thrombectomy for Acute Posterior Circulation Artery Occlusion: a Systematic Review and Meta-Analysis.

Luciano Falcão, Karina de Lima Andrade Pereira, Kenzo Ogasawara Donato, João Victor Pereira Gonzalez, Victor Arthur Ohannesian, Anderson Matheus Pereira da Silva, João Vitor Andrade Fernandes, Rafael Hummes Müller, Ocílio Ribeiro Gonçalves, Davi J F Solla and 2 more

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Clinical neuroradiology, 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

12 authors.

Luciano FalcãoEscola Bahiana de Medicina e Saúde Pública, Salvador, Brazil. luucmd@gmail.com.
Karina de Lima Andrade PereiraAnhembi Morumbi University, São Paulo, Brazil.
Kenzo Ogasawara DonatoEscola Bahiana de Medicina e Saúde Pública, Salvador, Brazil.
João Victor Pereira GonzalezEscola Bahiana de Medicina e Saúde Pública, Salvador, Brazil.
Victor Arthur OhannesianCenter of Interventional Radiology, Hospital Israelita Albert Einstein, São Paulo, Brazil.
Anderson Matheus Pereira da SilvaFederal University of Pernambuco, Recife, Brazil.
João Vitor Andrade FernandesFederal University of Paraíba, João Pessoa, Brazil.
Rafael Hummes MüllerEscola Bahiana de Medicina e Saúde Pública, Salvador, Brazil.
Ocílio Ribeiro GonçalvesFederal University of Piauí, Teresina, Brazil.
Davi J F SollaDepartment of Neurosurgery, Hospital do Subúrbio, Salvador, Brazil.
Hasan T OzgurDivision of Neuroradiology, Department of Radiology, University of Arizona, Tucson, United States.
Ahmet GünkanDivision of Vascular and Interventional Radiology, Department of Radiology, University of Arizona, Tucson, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMechanical thrombectomy (MT) is the standard treatment for acute posterior circulation artery occlusion (PCAO), but predicting outcomes remains challenging. Existing prognostic models combine clinical, imaging, and procedural variables but show inconsistent performance. Our objective is to evaluate the diagnostic accuracy of Machine Learning (ML) models in predicting favorable functional outcomes of thrombectomy for acute PCAO.

methodsWe conducted a systematic review and bivariate diagnostic meta-analysis of PubMed, Embase, and Web of Science. Eligible studies evaluated ML predicting favorable outcomes (Modified Rankin Scale of 0 to 3 at hospital discharge or at 90 days) after MT for PCAO. Pooled sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUC) were calculated with a bivariate random-effects model.

resultsFive studies including 1739 patients met inclusion criteria. Pooled sensitivity was 78% (95% CI: 59-89%; I

conclusionsML models demonstrate good diagnostic accuracy in predicting functional outcomes after thrombectomy for acute PCAO. Integration of these models into clinical practice may support individualized decision-making and resource allocation, although prospective validation and improved reporting are needed before routine implementation.

Indexed as

Arterial Occlusive DiseasesMachine LearningPredictive Learning ModelsThrombectomyHumansPrognosisSensitivity and SpecificityTreatment OutcomeMachine learningMechanical thrombectomyPosterior circulation artery occlusionStroke

Identifiers

PMID41801307

What OpenQuestion holds

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Registered trials

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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.