Alexithymia in radiologically isolated syndrome
Bibliographic record
Abstract
BACKGROUND: Alexithymia refers to difficulty identifying (DIF) and describing (DDF) feelings and externally oriented thinking (EOT). Its prevalence remains unknown in the radiologically isolated syndrome (RIS), the preclinical multiple sclerosis (MS) phase. METHODS: Alexithymia was measured with the Toronto Alexithymia Scale (TAS-20) in 29 RIS and age and gender-matched healthy controls and relapsing-remitting (RR) MS with an EDSS <3. All participants completed evaluations of cognition (BCCOG-SEP), depression (Fast-BDI), fatigue (EMIF), and quality of life (SEP-59). RESULTS: The level of alexithymia was significantly different between the three groups, with the higher score in the RRMS group (mean score of 54.5, SD: 12,3) compared to RIS (mean score of 47.2, SD: 14.8) and in healthy controls (mean score of 41.9, SD:12.8). 34 % of RIS participants showed a pathological level of alexithymia. The proportions were 21.7 % in the healthy controls and 51.7 % in the RRMS-matched groups. The difference was mainly significant for the DIF factor, p<.001. No significant correlations were observed between alexithymia and the different measures of cognition. In the RIS group, alexithymia was strongly linked to the levels of depression and cognitive fatigue. Furthermore, alexithymia was related to decreased mental quality of life. CONCLUSION: The study revealed that one-third of subjects with radiologically isolated syndrome show signs of alexithymia. Interestingly, no cognitive measure was found to be correlated with the level of alexithymia, which is consistent with previous research findings. Alexithymia and mainly difficulty identifying feelings in RIS are associated with depression but also relate to cognitive fatigue and reduced mental quality of life. This could impact the daily interactions of RIS subjects.
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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 | 0.001 |
| 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".