Evidence map›Paper›PMID 39236270›Full record

ArticleNeurology2024

Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods.

Jaime L Speiser, Wesley T Kerr, Andreas Ziegler

Abstract read
In one paragraph

Article in Neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Macroscale Gradient-Informed Neural Oscillation Topography in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026
    Article
  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

3 authors.

Jaime L SpeiserFrom the Department of Biostatistics and Data Science (J.L.S.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Neurology and Biomedical Informatics (W.T.K.), University of Pittsburgh, PA; Cardio-CARE (A.Z.), Medizincampus Davos, Switzerland; Department of Cardiology and Population Health Innovation (A.Z.), University Medical Center Hamburg-Eppendorf, Germany; and Department of Mathematics, Statistics and Computer Science (A.Z.), University of KwaZulu-Natal, Berea, Durban, South Africa.ORCID 0000-0003-0679-8730
Wesley T KerrFrom the Department of Biostatistics and Data Science (J.L.S.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Neurology and Biomedical Informatics (W.T.K.), University of Pittsburgh, PA; Cardio-CARE (A.Z.), Medizincampus Davos, Switzerland; Department of Cardiology and Population Health Innovation (A.Z.), University Medical Center Hamburg-Eppendorf, Germany; and Department of Mathematics, Statistics and Computer Science (A.Z.), University of KwaZulu-Natal, Berea, Durban, South Africa.ORCID 0000-0002-5546-5951
Andreas ZieglerFrom the Department of Biostatistics and Data Science (J.L.S.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Neurology and Biomedical Informatics (W.T.K.), University of Pittsburgh, PA; Cardio-CARE (A.Z.), Medizincampus Davos, Switzerland; Department of Cardiology and Population Health Innovation (A.Z.), University Medical Center Hamburg-Eppendorf, Germany; and Department of Mathematics, Statistics and Computer Science (A.Z.), University of KwaZulu-Natal, Berea, Durban, South Africa.ORCID 0000-0002-8386-5397

Funding

Development and Validation of a Prediction Model for Longitudinal Fall Risk in Older AdultsK25AG068253 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SPEISER, JAIME LYNN · 2021 to 2025
$578k
Feasibility of machine learning to improve the diagnostic odyssey for functional seizuresK23NS135134 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Wesley Thomas Kim Kerr · 2025 to 2026
$448k
NIA NIH HHS K25 AG068253NINDS NIH HHS K23 NS135134
6 · The paper itself

Abstract

Machine learning (ML) methods are becoming more prevalent in the neurology literature as alternatives to traditional statistical methods to address challenges in the analysis of modern data sets. Despite the increase in the popularity of ML methods in neurology studies, some authors do not fully address all items recommended in reporting guidelines. The authors of this Research Methods article are members of the

Indexed as

Machine LearningNeurologyBiomedical ResearchHumansResearch Design

Identifiers

PMID39236270
PMCPMC11379123

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.