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Record W4293139408 · doi:10.21203/rs.3.rs-1753416/v1

APOE E4 is associated with cognitive decline but not with disease risk or age of onset in Nigerians with Parkinson’s disease

2022· preprint· en· W4293139408 on OpenAlexaff
Njideka Okubadejo, Olaitan Okunoye, Oluwadamilola O. Ojo, Babawale Arabambi, Rufus Akinyemi, Godwin Osaigbovo, Sani Abubakar, Emmanuel Iwuozo, Kolawole Wahab, Osigwe Agabi, Uchechi Agulanna, Frank Imarhiagbe, Oladunni Abiodun, Charles Achoru, Akintunde Adebowale, Olaleye Adeniji, John E. Akpekpe, Mohammed Alli, Ifeyinwa Ani‐Osheku, Ohwotemu Arigbodi, Salisu A. Balarabe, Abiodun Bello, Oluchi Ekenze, Cyril Erameh, Temitope Farombi, Bimbo Fawale, Morenikeji Komolafe, Paul Nwani, Ernest Nwazor, Yakub Nyandaiti, Emmanuel Obehighe, Yahaya Obiabo, Olanike Odeniyi, Francis Odiase, FI Ojini, Gerald Onwuegbuzie, Nosakhare Osemwegie, Olajumoke Oshinaike, Folajimi Otubogun, Shyngle Oyakhire, Funlola Taiwo, Uduak Williams, Simon Ozomma, Yusuf Zubair, David Curtis, Dena Hernández, Sara Bandrés‐Ciga, Cornelis Blauwendraat, Andrew Singleton, Henry Houlden, John Hardy, Mie Rizig

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
FundersNational Institute on AgingNational Institutes of Health
KeywordsNigeriansCognitive declineDiseaseMedicineApolipoprotein EParkinson's diseaseCognitionDementiaCognitive impairmentGerontologyPsychiatryPsychologyInternal medicinePolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.367
Teacher spread0.313 · 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 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

Citations1
Published2022
Admission routes1
Has abstractno

Explore more

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