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Record W2234695709 · doi:10.1016/j.jalz.2015.12.003

Why has therapy development for dementia failed in the last two decades?

2015· article· en· W2234695709 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAlzheimer s & Dementia · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCenter for Diagnosis and Research on Alzheimer's Disease
FundersMedical Research CouncilNational Institute for Health and Care Research
KeywordsDementiaDrug developmentMedicineIntensive care medicineUnderpinningDiseaseNosologyPsychiatryDrugPathologyEngineering

Abstract

fetched live from OpenAlex

The success rate of the pharmaceutical research and development (R&D) for dementia drugs has been abysmally low, in the last two decades. Also low has been the number of pipeline drugs in development, compared to other therapy areas. However, the rationale of early terminations has not been reported in the majority of trials. These are key findings of the recently published pharmaceutical pipeline analysis by the UK-based Office of Health Economics (OHE). Our understanding of main challenges include (1) the significant gaps of knowledge in the nosology and complexity of the underpinning biological mechanisms of the commonest, not familial, forms of late onset dementias; (2) low signal-to-noise ratio, notwithstanding the lack of validated biomarkers as entry and/or end-point criteria; (3) recruitment and retention, particularly in the asymptomatic and early disease stages. A number of current and future strategies aimed at ameliorating drug development are outlined and discussed.

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.

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)0.016
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.487
GPT teacher head0.426
Teacher spread0.061 · 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