Non motor symptoms in patients with essential tremor
Bibliographic record
Abstract
Abstract Article Outline Abstract Introduction Patients and methods Procedure Statistical analysis Results Discussion Conclusion References Background Essential tremor (ET) is the most prevalent tremor disorder. ET has traditionally been viewed as a monosymptomatic disorder characterized by a kinetic arm tremor but now there is a growing amount of evidence to suggest that besides motor features, patients with essential tremor may exhibit significant non-motor features. The Aim of this study: To assess non-motor features in Egyptian patients with ET in comparison to controls. Patients and Methods This is a Case control observational study in which 30 patients with ET and 30 matched healthy controls ET was assessed using; Fahn Tolosa Marin Tremor Rating Scale (FTM); non-motor symptoms were evaluated using; Montreal Cognitive Assessment (MoCA), Beck Depression Inventory (BDI), Hamilton anxiety rating scale (HARS), Pittsburgh Sleep quality index (PSQI). Results ET patients had significant cognitive dysfunction, neuropsychiatric problems including depression, anxiety, poor sleep and other NMS compared to controls. Conclusion The presence of numerous non motor features in ET is increasingly evident. Therefore, NMS should be considered in the clinical evaluation and management of ET.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".