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Record W2890590605 · doi:10.1093/ageing/afy140.102

131TNF Inhibitors for the Prevention of Alzheimer’s Disease

2018· article· en· W2890590605 on OpenAlexaboutno aff
Bethany McDowell, Clive Holmes, Christopher J Edwards, Chris R. Cardwell, Michelle McHenry, G Meenagh, Bernadette McGuinness

Bibliographic record

VenueAge and Ageing · 2018
Typearticle
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAlzheimer's diseaseDiseaseDementiaIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Background: Alzheimer’s disease (AD) is the most common cause of dementia, affecting over 26 million people worldwide. A raised level of the proinflammatory cytokine, tumour necrosis factor-alpha (TNF-alpha) has been observed in the AD brain, highlighting the importance of neuroinflammation in the progression of AD and prompting the study of different anti-inflammatory treatments. TNF inhibitors (TNFi) are an established treatment for Rheumatoid Arthritis (RA); recently it was published that RA patients on TNFi have a lower incidence of dementia compared to those on traditional disease modifying anti-rheumatic drugs (DMARDs). Methods: This is a longitudinal observational study which will compare cognitive decline in RA participants with mild cognitive impairment (MCI) who are on TNFi to those on DMARDs. Participants > 55 years of age will be recruited from rheumatology clinics. Consenting participants will be screened using the Montreal Cognitive Assessment (MoCA). Those scoring ≤27/30 will be eligible to continue in the study as this cut off score is indicative of MCI. Cognitive assessments will be carried out at baseline using the Free and Cued Selective Reminding Test (FCSRT) and will be followed up at 6, 12 and 18 months. After the 18-month period, statistical analysis will be conducted to calculate the difference in mean FCSRT score between the TNFi and DAMRD groups. Results: The hypothesis for this study is that TNFi reduce the rate of cognitive decline in RA patients with MCI compared to DMARDs. Results of this study will be published in due course and shared with both AD and RA research communities at international conferences like the Alzheimer’s Association and the British society of Rheumatology. Conclusion: Without appropriate intervention the incidence of AD is estimated to rise dramatically in the coming years. If this study yields successful results, it would suggest the potential utility of TNFi as a preventative treatment for 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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.052
GPT teacher head0.334
Teacher spread0.282 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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