Clinical Profile of Non-Motor Symptoms in Patients with Essential Tremor: Impact on Quality of Life and Age-Related Differences
Bibliographic record
Abstract
Background: Identifying the clinical phenotypes of non-motor symptoms (NMSs) of essential tremor (ET) among different populations is necessary due to their impact on the quality of life (QoL). This study aimed to investigate the clinical phenotype and impact of NMSs on QoL in Egyptian patients with ET. Methods: Thirty ET patients were compared to 30 matched controls. Subjects were evaluated by the Fahn-Tolosa-Marin Tremor Rating Scale, Non-Motor Symptoms Scale (NMSS), Montreal Cognitive Assessment, Hamilton Anxiety Rating Scale, Beck Depression Inventory, Pittsburgh Sleep quality Index, and the Short Form 36 Health Survey Questionnaire. Both groups were divided into two subgroups of younger (<45 years, 14 patients) and older age (>45 years, 16 patients) groups, to investigate age-related differences. Results: ET patients showed significantly worse cognition, depression, anxiety, sleep and NMSS domains (p < 0.001), compared to controls, that negatively affected and predicted QoL. Older patients had more cognitive impairment (p = 0.003) and worse sleep/fatigue (p = 0.032) and sexual functions (p = 0.006), compared to younger group. Discussion: The study supports that NMSs are integral part of ET, negatively affect QoL, and similarly affect younger and older patients. Therefore, NMSs should be explored for proper care of ET patients.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".