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Record W2538541606 · doi:10.1016/j.trci.2017.01.001

The effect of statins on rate of cognitive decline in mild cognitive impairment

2017· article· en· W2538541606 on OpenAlexfundno aff
Kyle B. Smith, Paul Kang, Marwan N. Sabbagh

Bibliographic record

VenueAlzheimer s & Dementia Translational Research & Clinical Interventions · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingCanadian Institutes of Health ResearchGenentechNational Institutes of HealthH. Lundbeck A/SBarrow Neurological InstituteBioClinicaGE HealthcareNorthern California Institute for Research and EducationTakeda Pharmaceutical CompanyServierMerckU.S. Department of Defense
KeywordsCognitive impairmentCognitionCognitive declineMedicinePsychologyGerontologyInternal medicineClinical psychologyDementiaNeuroscienceDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: This study's aims are to identify whether or not a relationship between statin use and rate of cognitive decline exists. BACKGROUND: The relationship between statins and MCI has been investigated in the past with the evidence showing mixed results. METHODS: 768 subjects were identified with MCI. Subjects were stratified into 6 possible groups according to ApoE4 status and statin use and assessed for decline in cognitive function. RESULTS: All cognitive assessments trended towards less decline with statin use. ADAS11 showed the biggest difference in mean change between statin users and nonusers (-0.82 vs. -1.22 respectively). Change reached marginal significance on the ADAS11 when stratified by ApoE4 negative subjects. CONCLUSION: All cognitive assessments trended towards less decline when subjects were concurrently treated with a statin, supporting the position that statins do not have a net negative effect on cognitive assessment and suggests a potential treatment benefit.

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)0.014
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.232
GPT teacher head0.553
Teacher spread0.320 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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