In one paragraphArticle in medRxiv : the preprint server for health sciences, 2025. 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
15 authors.
Braden YangMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110.ORCID 0000-0002-2558-4132 Tom EarnestMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110.ORCID 0000-0001-8671-8424 Murat BilgelLaboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA 21224.ORCID 0000-0001-5042-7422 Marilyn S AlbertDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA 21205.ORCID 0000-0002-8167-8985 Sterling C JohnsonWisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA 53726.ORCID 0000-0002-8501-545X Christos DavatzikosArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-1025-8561 Guray ErusArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-6633-4861 Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA 21224.ORCID 0000-0003-1115-7145 Michael I MillerCenter of Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA 21218.ORCID 0000-0003-4689-3855 Arnold BakkerDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA 21205.ORCID 0000-0003-0417-641X John C MorrisDepartment of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110.ORCID 0000-0001-9820-5618 Tammie L S BenzingerMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110.ORCID 0000-0002-8114-0552 Brian A GordonMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110.ORCID 0000-0003-2109-2955 Aristeidis SotirasMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110.ORCID 0000-0003-0795-8820 Funding
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0MSmartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5MProject 2 MeasurementU19AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI Mei Cheng Wang · 2014 to 2026
$51.5MVascular factors, physical activity, and inflammation as modulators of neurodegenerative and cognitive trajectories (Project 2)P01AG036694 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI REISA A. SPERLING · 2010 to 2026
$50.2MSUPPLEMENT TO ALZHEIMERS DISEASE PATIENT REGISTRYU01AG006786 · NIA · MAYO CLINIC ROCHESTER · PI GRAFF-RADFORD, JONATHAN, JACK, CLIFFORD R. · 1986 to 2023
$49.6MThe natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5MWashington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4MTHE PGRN/TDP-43 AXIS IN ALZHEIMER?S DISEASE AND NEURODEGENERATIONP50AG016574 · NIA · MAYO CLINIC ROCHESTER · PI PETERSEN, RONALD C · 1999 to 2018
$36.9MWisconsin Registry for Alzheimer's Prevention: Sex Differences in DNA MethylationR01AG027161 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Nathaniel Ark Chin, Sterling C Johnson · 2007 to 2026
$35.6MWisconsin Registry for Alzheimer's PreventionRF1AG027161 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI JOHNSON, STERLING C · 2018 to 2018
$18.8MTHE XNAT IMAGING INFORMATICS PLATFORMR01EB009352 · NIBIB · WASHINGTON UNIVERSITY · PI Daniel Scott Marcus · 2009 to 2026
$9.3MPreclinical AD ConsortiumRF1AG059869 · NIA · JOHNS HOPKINS UNIVERSITY · PI ALBERT, MARILYN S. · 2018 to 2018
$7.7MNCATS NIH HHS UL1 TR000448NCRR NIH HHS S10 RR022984NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P01 AG036694NIA NIH HHS P50 AG016574NIA NIH HHS R01 AG027161NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG067103NIA NIH HHS RF1 AG027161NIA NIH HHS RF1 AG059869NIA NIH HHS U01 AG006786NIA NIH HHS U01 AG024904NIA NIH HHS U19 AG033655NIBIB NIH HHS R01 EB009352NIBIB NIH HHS T32 EB014855NIH HHS S10 OD018091NIH HHS S10 OD025200
6 · The paper itselfAbstract
Predicting the likelihood of developing Alzheimer's disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitment for clinical trials of disease-modifying therapies. Machine learning (ML) has been shown to excel in this task; however, there remains a lack of models developed specifically for the preclinical AD population, who display early signs of abnormal brain amyloidosis but remain cognitively unimpaired. Here, we trained and evaluated ML classifiers to predict whether individuals with preclinical AD will progress to mild cognitive impairment or dementia within multiple fixed time windows, ranging from one to five years. Models were trained on regional imaging features extracted from amyloid positron emission tomography and magnetic resonance imaging pooled across seven independent sites and from two amyloid radiotracers ([
Indexed as
amyloid positron emission tomographyclinical trial cohort enrichmentmachine learningpatient stratificationpreclinical Alzheimer’s diseasestructural magnetic resonance imagingsupport vector machine
Identifiers
PMID41282780
PMCPMC12633089
What OpenQuestion holds
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