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Record W2914378012 · doi:10.1038/s41531-018-0073-1

Basic clinical features do not predict dopamine transporter binding in idiopathic REM behavior disorder

2019· article· en· W2914378012 on OpenAlexaff
Lana M. Chahine, A. Iranzo, Ana Fernández‐Arcos, Tanya Simuni, Nicholas Seedorff, Chelsea Caspell‐Garcia, Amy W. Amara, Cynthia L. Comella, Birgit Högl, Jamie Hamilton, Kenneth Marek, Geert Mayer, Brit Mollenhauer, Ronald B. Postuma, E. Tolosa, Claudia Trenkwalder, Aleksandar Videnović, Wolfgang H. Oertel, Bhavna Kumar, Lynn James, George G. Nomikos, Jesse M. Cedarbaum, M. Yang, Mirosław Bryś, V. Irzhevesky, K. Schmidt, Nicholas R. Jennings, Alastair D. Reith, D. Tattersall, Mar M. Sánchez, Nichole Daegele, Chang‐Ki Min, Roneil G. Malkani, Judith Peterschmitt, P Sardi, Sylvie Bozzi, Tanya Fischer, Rachel Evans, Vera Kiyasova, Arthur A. Simen, Andrew Siderowf

Bibliographic record

Venuenpj Parkinson s Disease · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsMcGill University
FundersTeva Pharmaceutical IndustriesBiogenGlaxoSmithKlineServierPfizerEli Lilly and CompanySanofiMichael J. Fox Foundation for Parkinson's Research
KeywordsDopamine transporterREM sleep behavior disorderBiomarkerCohortDopaminePsychologyParkinson's diseaseRapid eye movement sleepDiseaseOncologyInternal medicineNeuroscienceMedicineAudiologyEye movementDopaminergicBiology

Abstract

fetched live from OpenAlex

Abstract REM sleep behavior disorder (RBD) is strongly associated with development of Parkinson’s Disease and other α-synuclein-related disorders. Dopamine transporter (DAT) binding deficit predicts conversion to α-synuclein-related disorders in individuals with RBD. In turn, identifying which individuals with RBD have the highest likelihood of having abnormal DAT binding would be useful. The objective of this analysis was to examine if there are basic clinical predictors of DAT deficit in RBD. Participants referred for inclusion in the RBD cohort of the Parkinson Progression Markers Initiative were included. Assessments at the screening visit including DAT SPECT imaging, physical examination, cognitive function screen, and questionnaire-based non-motor assessment. The group with DAT binding deficit ( n = 49) was compared to those without ( n = 26). There were no significant differences in demographic or clinical features between the two groups. When recruiting RBD cohorts enriched for high risk of neurodegenerative disorders, our data support the need for objective biomarker assessments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.001

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 head0.023
GPT teacher head0.303
Teacher spread0.281 · 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.

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

Citations32
Published2019
Admission routes1
Has abstractyes

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