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Record W7132492668

The Global Neurodegeneration Proteomics Consortium: biomarker and drug target discovery for common neurodegenerative diseases and aging

2025· article· en· W7132492668 on OpenAlexfundno aff

Bibliographic record

VenueDZNE Pub · 2025
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsnot available
FundersRobert Packard Center for ALS Research, Johns Hopkins UniversityNational Institute of Neurological Disorders and StrokeNational Institute on AgingSchool of Public Health, Imperial College LondonInstituto de Salud Carlos IIINational Center for Advancing Translational SciencesMedical Research CouncilWu Tsai Neurosciences Institute, Stanford UniversityParkinsonfondenUniversity of California, San FranciscoSchool of Medicine, Emory UniversityNational Institutes of HealthUniversité de MontréalCentro de Investigación Biomédica en Red sobre Enfermedades NeurodegenerativasSkånes universitetssjukhusAlzheimer NederlandKnut och Alice Wallenbergs StiftelseJanssen Research and DevelopmentGeneralitat de CatalunyaSchool of Medicine, Indiana UniversityHelsingin YliopistoGHR FoundationEuropean CommissionSun Health FoundationVanderbilt University Medical CenterLunds UniversitetJohns Hopkins UniversityKonung Gustaf V:s och Drottning Victorias FrimurarestiftelseEmory UniversityWellcome TrustUniversity College LondonImperial College LondonFaculty of Medicine and Health, University of SydneyVanderbilt UniversityScience for Life LaboratoryVetenskapsrådetAustralian Government
KeywordsNeurodegenerationAmyotrophic lateral sclerosisDiseaseProteomicsBiomarkerBiomarker discoveryFrontotemporal dementiaProteome
DOInot available

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.011
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0030.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.010
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0030.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.008

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 head0.017
GPT teacher head0.307
Teacher spread0.291 · 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
Published2025
Admission routes1
Has abstractyes

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