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Record W2601931132 · doi:10.1111/cns.12684

The neuroprotective effects of caffeine in neurodegenerative diseases

2017· review· en· W2601931132 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

VenueCNS Neuroscience & Therapeutics · 2017
Typereview
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsYork University
Fundersnot available
KeywordsCaffeineNeuroprotectionPharmacologyDiseaseAmyotrophic lateral sclerosisMedicineEstrogenAnimal studiesInternal medicine

Abstract

fetched live from OpenAlex

Summary Caffeine is the most widely used psychostimulant in Western countries, with antioxidant, anti‐inflammatory and anti‐apoptotic properties. In Alzheimer's disease ( AD ), caffeine is beneficial in both men and women, in humans and animals. Similar effects of caffeine were observed in men with Parkinson's disease ( PD ); however, the effect of caffeine in female PD patients is controversial due to caffeine's competition with estrogen for the estrogen‐metabolizing enzyme, CYP 1A2. Studies conducted in animal models of amyotrophic lateral sclerosis (ALS) showed protective effects of A 2 A R antagonism. A study found caffeine to be associated with earlier age of onset of Huntington's disease ( HD ) at intakes >190 mg/d, but studies in animal models have found equivocal results. Caffeine is protective in AD and PD at dosages equivalent to 3‐5 mg/kg. However, further research is needed to investigate the effects of caffeine on PD in women. As well, the effects of caffeine in ALS , HD and Machado‐Joseph disease need to be further investigated. Caffeine's most salient mechanisms of action relevant to neurodegenerative diseases need to be further explored.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0000.001
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 head0.147
GPT teacher head0.390
Teacher spread0.243 · 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