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Record W4390199928 · doi:10.1002/alz.072252

Diagnostic Performance of MRI‐based Alzheimer’s Disease Resemblance Atrophy Index and Plasma‐Based Biomarkers on Alzheimer’s Disease

2023· article· en· W4390199928 on OpenAlexaboutno aff
Siu Ting Fu, Yuan Cai, Junzhe Huang, Wanting Liu, Lin Shi, Lisa Au, Ho Ko, Vincent Mok

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaAtrophyBiomarkerAlzheimer's diseasePositron emission tomographyMedicineMontreal Cognitive AssessmentDiseaseCognitive declineCognitive impairmentNeuropsychologyPathologyPittsburgh compound BAmyloid (mycology)Internal medicineAlzheimer's Disease Neuroimaging InitiativeOncologyPathologicalCognitionNuclear medicinePsychiatryChemistry

Abstract

fetched live from OpenAlex

Abstract Background Although positron emission tomography (PET) or cerebrospinal fluid measurements of amyloid and tau burden are available for the detection of Alzheimer’s disease pathology, such methods are invasive and not easily accessible. Recent studies show that Alzheimer’s Disease‐resemblance atrophy index (AD‐RAI), an MRI‐based machine learning‐derived biomarker, and plasma‐based biomarkers can be used as accurate biomarkers for Alzheimer’s disease pathology. We aim to evaluate the diagnostic metrics of AD‐RAI and plasma‐based biomarkers for detecting Alzheimer’s disease. Method We recruited 69 subjects from the CU‐SEEDS (The Chinese University of Hong Kong‐Screening for Early AlzhEimer’s DiseaSe) study who were stroke‐free and had different degrees of cognitive impairment: 8 cognitive unimpaired [CU], 26 with subjective cognitive decline [SCD], 21 with mild cognitive impairment [MCI] and 14 with dementia. All subjects underwent 11C‐ PIB and 18F‐T807 PET to measure pathological Aβ deposition (A+) and tau burden (T+). Subjects received structural MRI for deriving AD‐RAI. Plasma levels of Aβ40, Aβ42, total tau (t‐tau), phosphorylated tau at 181 (p‐tau181), and neurofilament light chain (NfL) were measured by Single Molecule Array (SiMoA) assays. Result Among 69 subjects (mean [SD] age, 67.7 [6.8] years; 28 men [40.6%]), 25 (36.23%) subjects were confirmed to be A+ and T+. AD‐RAI, plasma p‐tau 181, and plasma Aβ42 were associated with A+T+ after adjusting for age, gender, and education (p<0.01). AD‐RAI individually outperformed all plasma‐based biomarkers (AUC = 0.911; Sensitivity = 0.826; Specificity = 0.905). The combination of AD‐RAI, p‐tau181 and Aβ42 yielded the best diagnostic metrics for detecting A+T+ subjects (AUC = 0.957; Sensitivity = 0.870; Specificity = 0.929). Conclusion A panel of AD‐RAI, plasma p‐tau, and plasma Aβ42 might help with screening and diagnosis of Alzheimer’s disease.

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 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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.034
GPT teacher head0.307
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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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