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Record W3013740472 · doi:10.1002/mds.28038

Progressive Supranuclear Palsy and Statin Use

2020· article· en· W3013740472 on OpenAlexaff
Ece Bayram, Connie Marras, David G. Standaert, Benzi M. Kluger, Yvette Bordelon, David Shprecher, Irene Litvan

Bibliographic record

VenueMovement Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersNational Institute on Aging
KeywordsPravastatinProgressive supranuclear palsyMedicineInternal medicineRating scaleAtorvastatinRosuvastatinClinical Dementia RatingEpworth Sleepiness ScalePhysical therapyPsychologyDementiaDiseaseCholesterol

Abstract

fetched live from OpenAlex

INTRODUCTION: Statins were proposed to be neuroprotective; however, the effects are unknown in progressive supranuclear palsy (PSP), a pure tauopathy. METHODS: Data of 284 PSP cases and 284 age-matched, sex-matched, and race-matched controls were obtained from the environmental and genetic PSP (ENGENE-PSP) study. Cases were evaluated with the PSP Rating Scale, Unified Parkinson's Disease Rating Scale, Mattis Dementia Rating Scale, and Neuropsychiatric Inventory. Statin associations with PSP risk, onset age, and disease features were analyzed. RESULTS: Univariate models showed lower PSP risk for type 1 statin users (simvastatin, lovastatin, pravastatin). After adjusting for confounding variables, statin use and lower PSP risk association remained only at a trend level. For PSP cases, type 1 statins were associated with 1-year older onset age; type 2 statins (atorvastatin, rosuvastatin) were associated with the lower PSP Rating Scale and Unified Parkinson's Disease Rating Scale. CONCLUSION: Statins may have inverse associations with PSP risk and motor impairment. Randomized prospective studies are required to confirm this effect. © 2020 International Parkinson and Movement Disorder Society.

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 score0.618
Threshold uncertainty score0.350

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)0.0000.000

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 head0.018
GPT teacher head0.250
Teacher spread0.231 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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