Bibliographic record
Abstract
Background: An independent psychological construct like alexithymia is the least studied in multiple sclerosis (MS). The objective of this study was to determine the level of alexithymia in patients with MS and the degree of influence on it of different social and demographic characteristics of patients, including gender, age, place of residence, marital status, level of education, clinical parameters of the disease, depression and anxiety. Materials and methods: 88 hospital patients with varying degrees of severity and type of MS were examined, according to the McDonald criteria, 2010. The following scales were used to assess the signs of depression, anxiety and alexithymia: the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory (STAI), the Toronto Alexithymia Scale (TAS-26). Results: The prevalence of alexithymia in patients with MS was 36.36%, while 34.09% represented the "marginal" group. A statistically significant positive correlation between alexithymia and depression and anxiety in patients with MS was established. High levels of alexithymia were detected with a high degree of depression on the BDI scale. None of the above socio-demographic and clinical variables influenced statistically significantly the presence of alexithymia. Conclusion: Alexithymia can be a key psychological factor that impedes the true emotional integration of disease-related changes.
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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) | 0.002 | 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".