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Record W2591158825 · doi:10.1016/j.jalz.2017.01.007

Consensus‐based recommendations for the management of rapid cognitive decline due to Alzheimer's disease

2017· article· en· W2591158825 on OpenAlexaff
Jianping Jia, Serge Gauthier, Sarah Pallotta, Yong Ji, Wenshi Wei, Shifu Xiao, Dantao Peng, Qihao Guo, Liyong Wu, Shengdi Chen, Weihong Kuang, Junjian Zhang, Cuibai Wei, Yi Tang

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityCenter for Diagnosis and Research on Alzheimer's Disease
FundersNovartis Pharma
KeywordsDementiaDiseaseCognitive declineMedicineCognitive reservePsychosisCognitionRandomized controlled trialPediatricsMini–Mental State ExaminationPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Rapid cognitive decline (RCD) occurs in dementia due to Alzheimer's disease (AD). METHODS: Literature review, consensus meetings, and a retrospective chart review of patients with probable AD were conducted. RESULTS: Literature review showed that RCD definitions varied. Mini-Mental State Examination scores <20 at treatment onset, vascular risk factors, age <70 years at symptom onset, higher education levels, and early appearance of hallucinations, psychosis, or extrapyramidal symptoms are recognized RCD risk factors. Chart review showed that RCD (Mini-Mental State Examination score decline ≥3 points/year) is more common in moderate (43.2%) than in mild patients (20.1%; P < .001). Rapid and slow decliners had similar age, gender, and education levels at baseline. DISCUSSION: RCD is sufficiently common to interfere with randomized clinical trials. We propose a 6-month prerandomization determination of the decline rate or use of an RCD risk score to ensure balanced allocation among treatment groups.

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)0.074
metaresearch head score (Gemma)0.153
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: Editorial · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.153
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0090.005
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0100.004
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.0060.004

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 head0.073
GPT teacher head0.380
Teacher spread0.307 · 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
GenreEditorial

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

Citations27
Published2017
Admission routes1
Has abstractyes

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