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Record W3012910544 · doi:10.1002/mds.28017

Defective Human Motion Perception in Cervical Dystonia Correlates With Coexisting Tremor

2020· article· en· W3012910544 on OpenAlexaff
Davide Martino, Gaia Bonassi, Giovanna Lagravinese, Elisa Pelosin, Giovanni Abbruzzese, Laura Avanzino

Bibliographic record

VenueMovement Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsCervical dystoniaDystoniaAudiologyPhysical medicine and rehabilitationPsychologyEssential tremorPerceptionMedicineNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND: The ability to predict temporal outcome of body movement is abnormal in idiopathic dystonia and can be altered by cerebellar neuromodulation. Tremor in cervical dystonia might be associated with performance on motion perception tasks. METHODS: A total of 15 cervical dystonia patients with and 14 without tremor and 15 age-matched healthy participants estimated the termination of videos showing different movements (handwriting a sentence, ball reaching a target) after these were darkened at different time intervals. RESULTS: = 4.57; P = 0.016). The percentage of responses in anticipation for both motion tasks did not differ across groups, suggesting lack of timing error directionality. CONCLUSIONS: Temporal processing of perceived motion in cervical dystonia is associated with the presence of tremor. Cortico-cerebellar network abnormalities in cervical dystonia might account for motion processing changes in these patients. © 2020 International Parkinson and Movement Disorder Society.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.262
Teacher spread0.242 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

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