Investigation of alexithymia and levels of anxiety and depression among patients with restless legs syndrome
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine alexithymia among restless legs syndrome (RLS) patients, compare with healthy controls, and argue the clinical inferences of this relationship. We searched for anxiety and depression and their clinical outcomes among patients and searched whether the results are similar to previous studies. PATIENTS AND METHODS: Eighty-seven RLS patients and 88 age, gender, and educationally matched healthy controls were assessed in Bezmialem Foundation University Hospital. RLS patients and healthy controls were assessed with the Sociodemographic Data Form constructed for the present study, 20-item Toronto Alexithymia Scale (TAS-20), Beck Depression Inventory (BDI), and Beck Anxiety Scale (BAS). The patient group was also assessed with the International Restless Legs Syndrome Study Group (IRLSSG) RLS Severity Scale. RESULTS: < 0.05). RLS severity score was positively correlated with the scores of anxiety and depression scales. However, no significant relationship was found between scores of IRLSSG RLS scale and TAS-20 total and subscale scores. CONCLUSION: RLS patients were found to be more alexithymic than healthy controls, whereas no significant relationship was found between RLS severity and levels of alexithymia. Still, alexithymia might be a predictor for early diagnosis and may be considered in the treatment and follow-up of RLS. RLS patients have higher depression and anxiety scores than healthy individuals. Thus, depression and anxiety should be taken into consideration throughout the RLS treatment.
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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 | 0.000 |
| 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".