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Record W6939389589 · doi:10.6084/m9.figshare.19697776

Reproducibility of Cerebellar involvement as quantified by consensus structural MRI biomarkers in Advanced Essential Tremor [Registered Report Stage 1 Protocol]

2022· article· en· W6939389589 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsEssential tremorNeuroimagingCerebellumMagnetic resonance imagingCerebellar ataxiaSusceptibility weighted imagingCerebellar diseasesReproducibility

Abstract

fetched live from OpenAlex

Abstract: Essential Tremor (ET) is the most prevalent movement disorder with poorly understood etiology. Some neuroimaging studies report cerebellar involvement, whereas others find no significant differences between ET and control groups. This discrepancy may stem from the underpowered studies as well as differences in Magnetic Resonance Imaging (MRI) acquisition and processing. To help resolve these differences, we plan to analyze the structural MRI scans from 1) an advanced ET cohort and normal controls (NC) acquired at the Montreal Neurological Institute and 2) additional NC subjects from PPMI and ADNI. We will test the hypothesis that the cerebellar involvement in advanced ET can be detected with multiple neuroimaging biomarkers: 1) cerebellar VBM, 2) cerebellar gray/white matter volumetry, and 3) cerebellar lobular volumetry. We will rigorously evaluate the sensitivity of the hypothesis tests to the underlying methods by varying image processing algorithms and confounder control design choices. Subsequently, we will also report the cortical changes associated with cerebellar “degeneration” in the advanced ET in an exploratory analysis.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reproducibility · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearch
Domain: Reproducibility · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.068
metaresearch head score (Gemma)0.103
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.932
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.103
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0130.007

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 head0.067
GPT teacher head0.380
Teacher spread0.313 · 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

Labeled directly by 2 models reading the full record.

Study designObservational
DomainReproducibility
GenreProtocol

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

Citations0
Published2022
Admission routes1
Has abstractyes

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