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Record W4406200873 · doi:10.1002/alz.093024

ALZpath pTau217: Alzheimer’s disease specificity in the context of multiple comorbidities and predictive capabilities for amyloid burden in combination with other blood‐based biomarkers

2024· article· en· W4406200873 on OpenAlexaff
Lauren Chaby, Jacob Borello, Timothy E. Vaughan, Stuart Portbury, Hans Frykman, Anna Mammel, Mary Joy Encarnacion, Geidy E. Serrano, Thomas G. Beach, Colin L. Masters, Christopher C. Rowe, Christopher Fowler, Rachael E. Wilson, Lianlian Du, Erin M. Jonaitis, Sterling C. Johnson, Andreas Jeromin

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)DiseaseAmyloid (mycology)MedicineAlzheimer's diseaseAmyloid βIntensive care medicineInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex

Abstract Background Tau phosphorylated at position 217 (pTau217) is considered to have the highest accuracy in identifying Alzheimer’s disease (AD) pathology using blood. We describe a multi‐cohort evaluation of the Simoa ALZpath pTau217 assay for the prediction of amyloid status in combination with additional blood‐based AD biomarkers (GFAP, pTau181, etc.), as well as comparisons between histopathological and PET based amyloid measurements. We describe the contribution of a spectrum of neurodegenerative diseases and demographic features to circulating plasma pTau217 levels. Method The Simoa ALZpath pTau217 assay is an ultra‐sensitive blood‐based assay developed on the semi‐automated single‐molecule array Simoa platform. We evaluate Simoa ALZpath pTau217 in the Banner Brain and Body Donation Program (characterized by several clinically meaningful comorbidities and mid‐to‐late stage AD; pTau217 subset: mean Braak Score = 4.17, ADNC High Plaques = 28%, ADNC Intermediate Plaques = 33%), the Wisconsin Registry for Alzheimer’s Prevention (a longitudinal cohort focused on healthy cognition through MCI), and a cognitively normal amyloid negative subset of the Australian Imaging, Biomarker and Lifestyle (AIBL) study. Result We discuss ALZpath pTau217 predictive capabilities in the context of PET imaging and post‐mortem histopathology for total and region‐specific pathological burden and the relative contribution of other blood‐based biomarkers and demographic features. We also report AD and MCI specificity in driving plasma levels of ALZpath pTau217 in the context of multiple comorbidities including CAA, DLB, and VAD. Conclusion Findings support ALZpath pTau217 as a high performing biomarker of CNS amyloid and tau burden that is driven specifically by AD and MCI in the context of multiple comorbidities. The ALZpath pTau217 performance capabilities can support timely AD diagnosis and intervention.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.284
Teacher spread0.250 · 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
Published2024
Admission routes1
Has abstractyes

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