Molecular Genetics of Essential Tremor
Bibliographic record
Abstract
Abstract Essential tremor (ET) is a movement disorder characterised by involuntary and rhythmic shaking, especially in the hands. The primary symptom associated with ET is an action tremor, which occurs with the purposeful use of a body part, like the hand. ET has a strong genetic component as 50–70% of cases have a familial history where transmission is typically autosomal dominant. The high rate of misdiagnosis, which is in part due to the absence of biomarkers, imaging or genetic markers, complicates the identification of genetic risk factors. Another challenging aspect is the overlapping clinical and pathological features between ET and Parkinson's disease (PD), along with the evidence of increased odds of developing PD when affected by ET. Decades of research have identified several ET loci and candidate genes, albeit replication studies, when available, yielded mixed results, and in most cases, evidence supporting variant causality remains poor. Nonetheless, these associations provide valuable insights into the complexity of the genetics underlying the disorder. Key Concepts Essential tremor is a neurological disorder characterised by involuntary and rhythmic shaking. The causes of essential tremor are unknown. Strong evidence supports high heritability for essential tremor. Decades of research have suggested multiple loci and genes associated with the disorder. Few of these genetic associations have been replicated in independent cohorts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".