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Record W2068385994 · doi:10.3389/fnagi.2015.00042

Are AD-Typical Regions the Convergence Point of Multiple Pathologies?

2015· review· en· W2068385994 on OpenAlexfundno aff
Sylvia Villeneuve, Miranka Wirth, Renaud La Joie

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2015
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsNeuroscienceDiseaseFront (military)Convergence (economics)MedicinePsychologyGerontologyPathologyGeographyEconomics

Abstract

fetched live from OpenAlex

The amyloid hypothesis proposes a serial model of causality whereby beta-amyloid (Aβ) initiates a cascade of negative events such as neurofibrillary tangle formation leading to neurodegeneration, and eventually clinical onset of Alzheimer’s disease (AD). While this hypothesis was mainly founded on genetic forms of AD observations, increasing results coming from Aβ imaging suggests that the reality for late-onset AD is more complex. Clearly, the disease develops in an older brain, where age-associated comorbid factors are more prevalent and therefore have a more significant influence on disease expression. Furthermore, it is well established that around one-third of cognitively normal older adults have abnormal Aβ accumulation in their brain (Aizenstein et al., 2008), indicating that Aβ alone might not be sufficient to lead to the clinical expression of late-onset AD. Most of the late-onset AD cases might therefore be the consequence of multi-factorial pathologies (Chételat, 2013).

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.133
GPT teacher head0.382
Teacher spread0.248 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations19
Published2015
Admission routes1
Has abstractyes

Explore more

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