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Record W3115148371 · doi:10.1016/j.bpsc.2020.12.007

Episodic Memory–Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer’s Disease: A Multicenter Study Based on Machine Learning

2020· article· en· W3115148371 on OpenAlexfundno aff
Yachen Shi, Zan Wang, Pindong Chen, Piaoyue Cheng, Kun Zhao, Hong‐Xing Zhang, Hao Shu, Lihua Gu, Lijuan Gao, Qīng Wáng, Haisan Zhang, Chunming Xie, Yong Liu, Zhijun Zhang

Bibliographic record

VenueBiological Psychiatry Cognitive Neuroscience and Neuroimaging · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthScience and Technology Planning Project of Guangdong ProvinceH. Lundbeck A/SGenentechIXICONational Natural Science Foundation of ChinaPfizerNovartis Pharmaceuticals CorporationUniversity of Southern CaliforniaGE HealthcareBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeNorthern California Institute for Research and EducationJohnson and Johnson Pharmaceutical Research and DevelopmentMerckAlzheimer's Drug Discovery FoundationNational Key Research and Development Program of ChinaAbbVieNational Institute on AgingAlzheimer's Association
KeywordsEpisodic memoryNeuroimagingMagnetic resonance imagingAlzheimer's Disease Neuroimaging InitiativePositron emission tomographyPsychologyCognitionAlzheimer's diseaseDiseaseCognitive impairmentNeuroscienceMedicineArtificial intelligencePathologyComputer scienceRadiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.356
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractno

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