MétaCan
Menu
Back to cohort
Record W2016732171 · doi:10.1002/mds.20467

Characterization of subclinical tremor in Parkinson's disease

2005· article· en· W2016732171 on OpenAlexafffund
Anne Beuter, Emilie Barbo, Robert Rigal, Pierre J. Blanchet

Bibliographic record

VenueMovement Disorders · 2005
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubclinical infectionResting tremorParkinson's diseaseMedicineEssential tremorPhysical medicine and rehabilitationAudiologyCentral nervous system diseaseDiseasePsychologyInternal medicine

Abstract

fetched live from OpenAlex

The physiological or pathological nature of subclinical tremor amplitude in Parkinson's disease (PD) is not well established. We analyzed characteristics of resting and postural tremors of subclinical amplitude in 17 patients with idiopathic PD without visible resting tremor, having a postural tremor in their least-affected hand rated 0 (12 subjects) or 1 (5 subjects) on Item 21 of the Unified Parkinson's Disease Rating Scale, compared to 17 control subjects matched for age, sex, and handedness. Tremor was recorded at the tip of the index finger using a displacement laser transducer. Overall results show that subclinical resting tremor in PD is significantly different from physiological tremor in terms of amplitude fluctuation, frequency dispersion, harmonic index, and proportional power in 4 to 6 Hz. No significant differences were found for postural tremor. These differences appear to originate mainly from patients with the mixed form of the disease. This study also confirms the preservation of physiological tremor likely originating from a distinct central oscillator in PD. The use of this method in the early and detailed characterization of PD tremors when amplitude is still within normal limits is proposed.

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 score0.034
Threshold uncertainty score0.346

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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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

Citations22
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueMovement DisordersSame topicNeurological disorders and treatmentsFrench-language works237,207