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Record W2157261330 · doi:10.1001/archneur.61.7.1057

Clustering of Parkinson Disease

2004· article· en· W2157261330 on OpenAlexaffabout
Ajit Kumar, Susan Calne, Michael Schulzer, Edwin Mak, Zbigniew K. Wszołek, Chris van Netten, Joseph Tsui, A. Jon Stoessl, Donald B. Calne

Bibliographic record

VenueArchives of Neurology · 2004
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British ColumbiaVancouver Hospital and Health Sciences CentreUniversity of British Columbia Hospital
Fundersnot available
KeywordsSpinocerebellar ataxiaDiseaseParkinson's diseaseCluster (spacecraft)PopulationCluster analysisGeneticsBiologyMedicineInternal medicineStatisticsMathematicsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The spatial and temporal pattern of excessive disease occurrence, termed clustering, may provide clues about the underlying etiology. OBJECTIVE: To report the occurrence of 3 clusters of Parkinson disease (PD) in Canada. DESIGN AND PATIENTS: We determined the population groups containing the clusters, geographical limits, and duration of exposure to the specific environments. We tested whether there was an excessive presence of Parkinson disease by calculating the probability of the observed cases occurring under the null hypothesis that the disease developed independently and at random in cluster subjects. Results of genetic testing for mutations in the alpha-synuclein, parkin, tau genes, and spinocerebellar ataxia genes (SCA2 and SCA3) were negative. RESULTS: The probabilities of random occurrence (P values) in the 3 clusters were P = 7.9 x 10 (-7)for cluster 1, P = 2.6 x 10 (-7)for cluster 2, and P = 1.5 x 10 (-7)for cluster 3. CONCLUSIONS: Our findings indicate an important role for environmental causation in Parkinson disease. A possible role exists for environmental factors such as viral infection and toxins in the light of current evidence.

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 score0.272
Threshold uncertainty score0.308

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 head0.015
GPT teacher head0.257
Teacher spread0.241 · 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

Citations38
Published2004
Admission routes2
Has abstractyes

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