Reproducibility of Cerebellar involvement as quantified by consensus structural MRI biomarkers in Advanced Essential Tremor [Registered Report Stage 1 Protocol]
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".