Alexithymia, Anger, Sensitivity to Mild Bodily Sensations and Personality Characteristics of Restless Legs Syndrome Patients: A Case-control Study
Bibliographic record
Abstract
OBJECTIVE: This study aims to asess alexithymia, anger and its expression, sensitivity to bodily sensations, personality, and their relationship with the severity of the disease in patients with Restless Legs Syndrome. METHOD: The study included 63 patients diagnosed with Restless Legs Syndrome and 63 age, gender and education matched controls. All participants were given, Toronto Alexithymia Scale, Somatosensory Amplification Scale, The State Trait Anger Scale and Temperament and Character Inventory. The severity of Restless Legs Syndrome was evaluated using the Restless Legs Syndrome Severity Rating Scale. RESULTS: The trait anger score (p=0,001), sensitivity to bodily sensations (p<0,001), and the total score of alexithymia (p<0,001) were significantly higher in the patient group Reward dependence in patient group was significantly higher (p=0,008). Restless Legs Syndrome severity positively correlated with trait anger level (r=0,360; p=0,015) and alexithymia total score (r=0,373; p=0,003). Restless Legs Syndrome severity negatively correlated with self-directedness (r=-0,323; p=0,010). CONCLUSION: We demonstrated that alexithymia, sensitivity to body sensations, and anger was high in restless leg syndrome in Restless Legs Syndrome. Restless Legs Syndrome is linked to physical and mental symptoms and certain personality traits.
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
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".