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Record W3021660162 · doi:10.1002/mdc3.12966

How Do I Examine Patients With Functional Tremor?

2020· article· en· W3021660162 on OpenAlexaff
Sarah C. Lidstone, Anthony E. Lang

Bibliographic record

VenueMovement Disorders Clinical Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsParkinson's Clinic of Eastern Toronto & Movement Disorders CentreToronto Western Hospital
Fundersnot available
KeywordsPhysical medicine and rehabilitationNeurophysiologyElectromyographyPsychologyEssential tremorElectroencephalographyMedicineNeuroscience

Abstract

fetched live from OpenAlex

Functional tremor is the most common presentation of functional movement disorders and can occur in isolation or together with other functional symptoms, including other abnormal movements. The diagnosis of functional tremor is based on positive features on history, examination, and, if necessary, neurophysiological studies. Historical features include: sudden onset, a preceding physical event or injury, variability in severity with or without remission, variability in affected body parts, the presence of other somatic symptoms, and a history of failed therapeutic trials. Positive signs on examination include: variability in the frequency, direction, and distribution of the tremor; clear coherence in the different body parts affected; reduction or elimination of the tremor with distraction; and tremor amplification with attention, entrainability, suggestibility, and the presence of co-contraction. Neurophysiological studies include electromyography and accelerometry and can be helpful to make a laboratory-supported diagnosis when the clinical picture is less clear.

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 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)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0040.008
Open science0.0020.001
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0100.010

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 head0.057
GPT teacher head0.317
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations8
Published2020
Admission routes1
Has abstractyes

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