MétaCan
Menu
Back to cohort
Record W1991537160 · doi:10.1002/mds.23934

Association of <i>SNCA</i> with Parkinson: Replication in the Harvard NeuroDiscovery Center Biomarker Study

2011· article· en· W1991537160 on OpenAlexaff
Hongliu Ding, Alison K. Sarokhan, Sarah S. Roderick, Rachit Bakshi, Nancy Maher, Paymon Ashourian, Caroline G. Kan, Sunny Chun Chang, Andrea Santarlasci, Kyleen Swords, Bernard Ravina, Michael T. Hayes, U. Shivraj Sohur, Anne‐Marie Wills, Alice W. Flaherty, Vivek K. Unni, Albert Y. Hung, Dennis J. Selkoe, Michael A. Schwarzschild, Michael G. Schlossmacher, Lewis Sudarsky, John H. Growdon, Adrian J. Ivinson, Bradley T. Hyman, Clemens R. Scherzer

Bibliographic record

VenueMovement Disorders · 2011
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Ottawa
FundersNational Institute of Neurological Disorders and StrokeHarvard NeuroDiscovery Center
KeywordsOdds ratioDiseaseGeneticsGenome-wide association studyAlleleBiomarkerParkinson's diseaseCase-control studyLocus (genetics)Genetic associationGenotypeMinor allele frequencyBiologyMedicineOncologySingle-nucleotide polymorphismAllele frequencyGeneInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mutations in the α-synuclein gene (SNCA) cause autosomal dominant forms of Parkinson's disease, but the substantial risk conferred by this locus to the common sporadic disease has only recently emerged from genome-wide association studies. METHODS: We genotyped a prioritized noncoding variant in SNCA intron 4 in 344 patients with Parkinson's disease and 275 controls from the longitudinal Harvard NeuroDiscovery Center Biomarker Study. RESULTS: The common minor allele of rs2736990 was associated with elevated disease susceptibility (odds ratio, 1.40; P = .0032). CONCLUSIONS: This result increases confidence in the notion that in many clinically well-characterized patients, genetic variation in SNCA contributes to "sporadic" disease.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.338

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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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

Citations25
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMovement DisordersSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207