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Record W3030244056 · doi:10.1136/jnnp-2020-323189

Tremor: so common, so difficult

2020· letter· en· W3030244056 on OpenAlexaff
María Carolina Sepúlveda Soto, Alfonso Fasano

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2020
Typeletter
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsOntario Brain InstituteToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsEssential tremorNeurologyContext (archaeology)Movement disordersPsychologyMedicinePhysical medicine and rehabilitationSign (mathematics)PsychiatryHistoryPathologyDisease

Abstract

fetched live from OpenAlex

Whether you are a general practitioner or a movement disorders specialist, you have seen patients with tremor, the most frequent movement disorder and the most common sign in clinical neurology.1 In spite of its prevalence, tremor is a complex and often misdiagnosed disorder, especially by non-specialists. Furthermore, a number of false beliefs still populate our textbooks and are taught in medical schools (box 1). In this context, the review by van de Wardt et al 2 is a very timely exercise in light of the recent new classification of tremor.3 Box 1 ### Common false beliefs and pitfalls in tremor ET, …

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)0.001
metaresearch head score (Gemma)0.015
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0040.003

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 head0.020
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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2020
Admission routes1
Has abstractyes

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