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Record W3131759917 · doi:10.4103/1673-5374.308070

Alzheimer’s disease: a tale of two diseases?

2021· review· en· W3131759917 on OpenAlexaff
Gilbert Bernier, Eleonora Nardini, Ryan Hogan, Anthony Flamier

Bibliographic record

VenueNeural Regeneration Research · 2021
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPSEN1DiseaseNeuroscienceClinical trialAlzheimer's diseasePresenilinMedicineBiologyBioinformaticsPathology

Abstract

fetched live from OpenAlex

Sporadic late-onset Alzheimer’s disease (SLOAD) and familial early-onset Alzheimer’s disease (FEOAD) associated with dominant mutations in APP , PSEN1 and PSEN2 , are thought to represent a spectrum of the same disorder based on near identical behavioral and histopathological features. Hence, FEOAD transgenic mouse models have been used in past decades as a surrogate to study SLOAD pathogenic mechanisms and as the gold standard to validate drugs used in clinical trials. Unfortunately, such research has yielded little output in terms of therapeutics targeting the disease’s development and progression. In this short review, we interrogate the widely accepted view of one, dimorphic disease through the prism of the Bmi1 +/– mouse model and the distinct chromatin signatures observed between SLOAD and FEOAD brains.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.244
GPT teacher head0.504
Teacher spread0.260 · 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 designNot applicable
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

Citations26
Published2021
Admission routes1
Has abstractyes

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