Is impairment of facial emotion recognition independent of cognitive dysfunction in multiple sclerosis?
Bibliographic record
Abstract
BACKGROUND: Emotions expressed on the face play a key role in social cognition and communication by providing inner emotional experiences. This study aimed to evaluate facial emotion identification and discrimination and empathy abilities in patients with MS and whether it is related to cognitive dysfunction. METHODS: One hundred twenty patients with relapsing-remitting MS and age- and sex-matched 120 healthy controls were enrolled in the study. All the subjects were evaluated with the Facial Emotion Identification Test (FEIT), Facial Emotion Discrimination Test (FEIDT), and Empathy Quotient (EQ). We used the Beck Depression Inventory (BDI) for depression and detailed cognitive tests, including the Montreal Cognitive Assessment (MoCA), the Symbol Digit Modalities Test (SDMT), and the Paced Auditory Serial Addition Test (PASAT). The quality of life was assessed with Multiple Sclerosis Quality of Life-54 (MSQL-54). RESULTS: Patients with MS were 37.6 ± 9.5 years old, had a mean disease duration of 8.8 ± 6.6 (8-28) years, and a mean EDSS score of 1.6 ± 1.3 (0-4.5). We found significant differences in the identification of facial emotions, discrimination of facial emotions, and empathy in MS patients compared to controls (p < 0.05). Especially the recognition of feelings of sadness, fear, and shame was significantly lower in MS patients. The multivariate logistic regression analysis showed low SDMT and FEIDT scores which showed an independent association with MS. CONCLUSIONS: Our findings indicate that facial emotion recognition and identification deficits are remarkable among patients with MS and emotion recognition is impaired together with and independently of cognitive dysfunction in MS patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".