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Record W3021660162 · doi:10.1002/mdc3.12966

How Do I Examine Patients With Functional Tremor?

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
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 score0.212
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.057
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