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Record W2966191040 · doi:10.1016/j.jalz.2019.05.006

Frequency and longitudinal clinical outcomes of Alzheimer's AT(N) biomarker profiles: A longitudinal study

2019· article· en· W2966191040 on OpenAlexfundno aff
Jin‐Tai Yu, Jie‐Qiong Li, John Suckling, Lei Feng, An Pan, Yan‐Jiang Wang, Bo Song, Shan‐Liang Zhu, De‐Hu Li, Hui‐Fu Wang, Chen‐Chen Tan, Qiang Dong, Lan Tan, Vincent Mok, Paul Aisen, Michael M. Weiner

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingCanadian Institutes of Health ResearchNational Institutes of HealthU.S. Department of DefenseFudan UniversityScience and Technology Commission of Shanghai MunicipalityNational Institute of Biomedical Imaging and BioengineeringNorthern California Institute for Research and EducationNational Natural Science Foundation of China
KeywordsHazard ratioProportional hazards modelBiomarkerInternal medicineLongitudinal studyDementiaClinical trialNeurodegenerationMedicineMultivariate statisticsOncologyDiseaseMultivariate analysisPsychologyPathologyConfidence intervalBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: We aimed to estimate the frequency of each AT(N) (β-amyloid deposition [A], pathologic tau [T], and neurodegeneration [N]) profile in different clinical diagnosis groups and to describe the longitudinal change in clinical outcomes of individuals in each group. METHODS: Longitudinal change in clinical outcomes and conversion risk of AT(N) profiles are assessed using linear mixed-effects models and multivariate Cox proportional-hazard models, respectively. RESULTS: Participants with A+T+N+ showed faster clinical progression than those with A-T-N- and A+T±N-. Compared with A-T-N-, participants with A+T+N± had an increased risk of conversion from cognitively normal (CN) to incident prodromal stage of Alzheimer's disease (AD), and from MCI to AD dementia. A+T+N+ showed an increased conversion risk when compared with A+T±N-. DISCUSSION: The 2018 research framework may provide prognostic information of clinical change and progression. It may also be useful for targeted recruitment of participants with AD into clinical trials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.092
GPT teacher head0.394
Teacher spread0.302 · 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 teacher head, not a consensus.

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

Citations80
Published2019
Admission routes1
Has abstractyes

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