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Record W2339438796 · doi:10.5935/0103-5053.20160048

Current Approaches Against Alzheimer's Disease in Clinical Trials

2016· article· en· W2339438796 on OpenAlexaff
Kamil Kuča, Ondřej Soukup, Petra Marešová, Jan Korábečný, Eugenie Nepovimová, Blanka Klímová, Jan Honegr, Teodorico C. Ramalho, Tanos C. C. França

Bibliographic record

VenueJournal of the Brazilian Chemical Society · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Regional Competitiveness
Canadian institutionsConcordia University
Fundersnot available
KeywordsCurrent (fluid)DiseaseClinical trialMedicineIntensive care medicineInternal medicineEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is a progressive degenerative brain disease which causes mental and physical decline, gradually resulting in death. Currently, this disease represents one of the uppermost human issues, both from the medical and economic point of view. Interest in the discovery of a drug for AD is enormous. However, despite the long-term and worldwide effort for a more effective therapy, the only available treatment is a symptomatic use of acetylcholinesterase inhibitors (AChEIs) and memantine. New therapeutic approaches as well as those based on cholinergic or amyloid theory have not brought the desired benefits yet. Thus, the question is whether an effective drug for this progressive disease will ever be developed or whether people will have to rely only on prevention and minimize risk factors of AD.

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)0.010
metaresearch head score (Gemma)0.012
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 score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0350.006

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 head0.205
GPT teacher head0.330
Teacher spread0.125 · 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

Citations54
Published2016
Admission routes1
Has abstractyes

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