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Record W4211165247 · doi:10.1016/j.cccb.2022.100044

What does aducanumab treatment of Alzheimer's disease mean for research on vascular cognitive disorders?

2022· editorial· en· W4211165247 on OpenAlexaff
Anders Wallin, Suvarna Alladi, Sandra E. Black, Christopher Chen, Steven M. Greenberg, Deborah Gustafson, Jeremy D. Isaacs, Hanna Jokinen, Raj N. Kalaria, Vincent Mok, Leonardo Pantoni, Florence Pasquier, Gustavo C. Román, Gary A. Rosenberg, Reinhold Schmidt, Eric E. Smith, Atticus H. Hainsworth

Bibliographic record

VenueCerebral Circulation - Cognition and Behavior · 2022
Typeeditorial
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsUniversity of CalgaryHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersMedical Research CouncilAgence Nationale de la Recherche
KeywordsObservational studyCognitionDiseaseCognitive impairmentMedicineAlzheimer's diseasePsychologyVascular dementiaDementiaPsychiatryClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

•Controversial registration of aducanumab for Alzheimer's Disease•Aducanumab is the subject of post-licensing observational studies aiming to follow the effects of the drug•Given the high prevalence of cerebrovascular pathology it is important that these studies do not ignore vascular cognitive disorders•The studies may give detailed phenotyping data that may lead to knowledge of targets for treatments of patients with vascular cognitive disorders.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.122
GPT teacher head0.388
Teacher spread0.267 · 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 teacher head, not a consensus.

Study designOther design
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

Citations10
Published2022
Admission routes1
Has abstractyes

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