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Record W3016882932 · doi:10.3233/jad-200022

Plasma Neurofilament Light and Longitudinal Progression of White Matter Hyperintensity in Elderly Persons Without Dementia

2020· article· en· W3016882932 on OpenAlexfundno aff
Yan Sun, Lin Tan, Wei Xu, Zuo-Teng Wang, Hao Hu, Jieqiong Li, Qiang Dong, Lan Tan, Jin‐Tai Yu

Bibliographic record

VenueJournal of Alzheimer s Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
FundersNational Institute on AgingCanadian Institutes of Health Research
KeywordsHyperintensityDementiaNeurofilamentWhite matterMedicineLongitudinal studyPsychologyGerontologyNeuroscienceInternal medicinePathologyMagnetic resonance imagingDiseaseRadiology

Abstract

fetched live from OpenAlex

White matter hyperintensities (WMH) is mainly caused by cerebrovascular injury and may also increase the possibilities of progression to Alzheimer's disease. The present study aims to determine whether plasma neurofilament light (NFL) protein levels could predict the progression of WMH volume in elderly persons without dementia. The present study enrolled 1029 non-dementia participants from the Alzheimer's Disease Neuroimaging Initiative in which all had measurements of plasma NFL and WMH at baseline and 589 had longitudinal measurements during follow-up. Spearman correlation analyses and regression models were used to assess cross-sectional and longitudinal associations between plasma NFL and WMH. Plasma NFL concentration had a moderately strong correlation with WMH at baseline (r = 0.17, p < 0.001). Longitudinal analyses showed that higher baseline plasma NFL concentration was associated with accelerated progression of WMH (β=0.015, p = 0.007). Furthermore, higher change rates of plasma NFL could predict faster progression of WMH in the future (β=0.581, p = 0.002). The results of the study suggest that plasma NFL level might be used as a noninvasive biomarker to track variation trend in WMH in elderly persons without dementia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.066
GPT teacher head0.340
Teacher spread0.274 · 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.

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

Citations27
Published2020
Admission routes1
Has abstractyes

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