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Biofluid markers of blood-brain barrier disruption and neurodegeneration in Lewy body spectrum diseases: A systematic review and meta-analysis

2022· review· en· W4283156230 on OpenAlexafffund
Yuen Yan Wong, Che‐Yuan Wu, Di Yu, Esther Kim, Melissa H. Wong, Renata Elez, Julia Zebarth, Michael Ouk, Jocelyn Tan, Jiamin Liao, Eileen Haydarian, Siming Li, Yaolu Fang, Peihao Li, Maureen Pakosh, Maria Carmela Tartaglia, Mario Masellis, Walter Swardfager

Bibliographic record

VenueParkinsonism & Related Disorders · 2022
Typereview
Languageen
FieldNeuroscience
TopicBarrier Structure and Function Studies
Canadian institutionsOccupational Cancer Research CentreToronto Rehabilitation InstituteUniversity of TorontoOntario Brain InstituteSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchNational Institutes of HealthCanada Research ChairsWeston Brain InstituteNatural Sciences and Engineering Research Council of CanadaFondation Brain CanadaAlzheimer's AssociationOntario Brain InstituteUniversity of WashingtonMichael J. Fox Foundation for Parkinson's Research
KeywordsDementia with Lewy bodiesDementiaLewy bodyInternal medicineMeta-analysisMedicineNeurodegenerationConfidence intervalBlood–brain barrierParkinson's diseaseGastroenterologyPathologyDiseaseCentral nervous system

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.020
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.287
Teacher spread0.256 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations42
Published2022
Admission routes2
Has abstractno

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