Evidence map›Paper›PMID 38384375›Full record

ArticleFrontiers in computational neuroscience2024

Random forest analysis of midbrain hypometabolism using [

Marina C Ruppert-Junck, Gunter Kräling, Andrea Greuel, Marc Tittgemeyer, Lars Timmermann, Alexander Drzezga, Carsten Eggers, David Pedrosa

Open access · goldAbstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
2.6field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Pooled it
  2. Pooled it
  3. Review
  4. 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

8 authors at 7 institutions in 1 country.

Marina C Ruppert-JunckDepartment of Neurology, Philipps-University of Marburg, Marburg, Germany.
Gunter KrälingClinic for Neurology, University Hospital Gießen and Marburg GmbH, Marburg, Germany.
Andrea GreuelDepartment of Psychiatry, Psychotherapy and Psychosomatics, Vivantes Hospital Neukölln, Berlin, Germany.
Marc TittgemeyerMax Planck Institute for Metabolism Research, Cologne, Germany.
Lars TimmermannDepartment of Neurology, Philipps-University of Marburg, Marburg, Germany.
Alexander DrzezgaCluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany.
Carsten EggersDepartment of Neurology, Philipps-University of Marburg, Marburg, Germany.
David PedrosaDepartment of Neurology, Philipps-University of Marburg, Marburg, Germany.
Universitätsklinikum Gießen und Marburg · DEBrain (Germany) · DEJustus-Liebig-Universität Gießen · DEMax Planck Institute for Metabolism Research · DEPhilipps University of Marburg · DEUniversity Hospital Cologne · DEVivantes Klinikum · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [

Indexed as

imaging biomarkermachine learningmetabolic imagingParkinson's diseaserandom forest

Identifiers

PMID38384375
PMCPMC10879348
OpenAlexW4391609425

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.