In one paragraphArticle in The journals of gerontology. Series A, Biological sciences and medical sciences, 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
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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors.
Phoebe ImmsLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States.ORCID 0000-0002-7205-4177 Haoqing WangLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States.ORCID 0009-0008-3929-7864 Samayan BhattacharyaDepartment of Biomedical Engineering, Corwin D. Denney Research Center, Viterbi School of Engineering, University of Southern California, Los Angeles, California, United States.ORCID 0000-0002-2725-5367 Nikhil N ChaudhariLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States.ORCID 0000-0003-3048-6710 Owen M VegaLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States.ORCID 0009-0000-6106-8154 Jorge A Solis GalvanDepartment of Biomedical Engineering, Corwin D. Denney Research Center, Viterbi School of Engineering, University of Southern California, Los Angeles, California, United States.ORCID 0000-0001-5327-1443 Siyu ChenLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States.ORCID 0000-0002-8874-2644 Ruixi LiCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, New York, United States.ORCID 0009-0000-3071-4678 Andrei IrimiaLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States.ORCID 0000-0002-9254-9388 Funding
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8MResearch Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Katherine P Rankin · 2019 to 2026
$36.9MResearch Education ComponentP30AG062421 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Christine S Ritchie · 2019 to 2026
$36.5MUCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DOUGLAS R GALASKO · 2019 to 2026
$34.9MWisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Sanjay Asthana · 2019 to 2026
$34.5MResearch Education ComponentP30AG062677 · NIA · MAYO CLINIC ROCHESTER · PI KEJAL KANTARCI · 2019 to 2026
$33.5MResearch Education ComponentP30AG066514 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Margaret Sewell · 2020 to 2026
$31.0MYale Alzheimer Disease Research CenterP30AG066508 · NIA · YALE UNIVERSITY · PI STEPHEN M STRITTMATTER · 2020 to 2026
$30.2MResearch Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PHILIP L DE JAGER · 2020 to 2026
$30.1MResearch Education ComponentP30AG066468 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI C. Elizabeth Shaaban · 2020 to 2026
$29.4MResearch Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3MUniversity of Washington Alzheimer's Disease Research CenterP30AG066509 · NIA · UNIVERSITY OF WASHINGTON · PI Amanda D. Boyd · 2020 to 2026
$29.0MNIA NIH HHS P20 AG068024NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG 017265NIA NIH HHS P30 AG017265NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066462NIA NIH HHS P30 AG066468NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066508NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG066519NIA NIH HHS P30 AG066530NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072946NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072977NIA NIH HHS P30 AG072978NIA NIH HHS P30 AG072979NIA NIH HHS R01 AG079280NIA NIH HHS R01 AG079957NIA NIH HHS RF1 AG082201NIA NIH HHS T32 AG000037NIA NIH HHS U24 AG072122NINDS NIH HHS R01 NS100973
6 · The paper itselfAbstract
backgroundPredicting Alzheimer's disease (AD)-related cognitive impairment (CI) among cognitively normal (CN) adults enables meaningful disease modification through early intervention and enrichment of clinical trials.
methodsA deep survival model is trained to predict CI conversion risk in 1415 CN adults from the National Alzheimer's Coordinating Center. Converters' (N = 212) and non-converters' (N = 1203) baseline clinical measures and magnetic resonance images are used to estimate their conversion probability up to 22 years after baseline observation.
resultsAfter 20-fold cross-validation, the model predicts conversion probability with a c-index of 0.88, classification accuracy of 75%, and AUC ROC of 0.89, outperforming previous machine learning models.
conclusionsThis is one of few studies on the important challenge of predicting future CI among unimpaired subjects. Deep survival modeling can improve the identification of preclinical AD and suggests that uncertainty in AD risk estimation is due to potentially modifiable lifestyle factors.
Indexed as
Alzheimer DiseaseCognitive DysfunctionDeep LearningAgedDisease ProgressionFemaleHumansMagnetic Resonance ImagingMalePrediction AlgorithmsPredictive Learning ModelsRisk AssessmentAlzheimer’s diseaseCognitive impairmentDeep learningMagnetic resonance imagingSurvival modeling
Identifiers
PMID41844537
PMCPMC13180249
What Socratic holds
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