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Record W4223486009 · doi:10.3233/jad-220262

Making the Case for Accelerated Withdrawal of Aducanumab

2022· article· en· W4223486009 on OpenAlexaff
Peter J. Whitehouse, Sam Gandy, Vikas Saini, Daniel R. George, Eric B. Larson, G. Caleb Alexander, Jerry Avorn, Shannon Brownlee, Cameron J. Camp, Howard Chertkow, Adriane Fugh‐Berman, Robert Howard, Aaron S. Kesselheim, Kenneth M. Langa, George Perry, Edo Richard, Lon S. Schneider

Bibliographic record

VenueJournal of Alzheimer s Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill UniversityBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsAllianceFood and drug administrationDementiaOpposition (politics)MedicinePolitical sciencePsychologyFamily medicinePublic relationsDiseaseLawPharmacologyPathology

Abstract

fetched live from OpenAlex

The controversial approval in June 2021 by the Food and Drug Administration (FDA) of aducanumab (marketed as Aduhelm), Biogen's monoclonal antibody for patients with Alzheimer's disease, raises significant concerns for the dementia field and drug approval process, considering its lack of adequate evidence for clinical efficacy, safety issues, and cost. On 15 December 2021, an international group of clinicians, basic science experts, psychological and social science researchers, lay people with lived experience of dementia, and advocates for public health met to discuss making a recommendation for whether aducanumab's approval should be withdrawn. Attendees considered arguments both in favor of and in opposition to withdrawal and voted unanimously to recommend that the FDA withdraw its approval for aducanumab and to support the Right Care Alliance's filing of a formal Citizen Petition to this effect.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.597

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 studies0.0000.000
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 head0.089
GPT teacher head0.379
Teacher spread0.290 · 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.

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

Citations40
Published2022
Admission routes1
Has abstractyes

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