Neck and mind: exploring emotion processing in cervical dystonia
Bibliographic record
Abstract
Objective A wide range of non-motor symptoms such as pain, mood disorders, insomnia, and executive dysfunction may occur in focal dystonia. Little is known, however, about emotional processing. We aim to assess emotion recognition and alexithymia in patients with cervical dystonia (CD) compared to healthy age-, sex- and education-matched controls (HC). Methods Emotion processing was assessed with an eye-tracking paradigm using a validated dataset of facial expressions and the Toronto Alexithymia Scale (TAS-20). Dystonia severity and disability, cognition, and comorbid depression and anxiety were also assessed. Results We recruited 35 CD patients and 17 matched HC. In the eye-tracking task, CD patients recognized emotions less accurately than HCs (77.0% vs. 84.4%; p = 0.001), primarily based on difficulties in identification of fear (p = 0.003) and surprise (p = 0.037). Moreover, patients had longer fixations within the mouth region (p = 0.027) and left eye (p = 0.037) than HC. CD patients also had significantly higher total TAS-20 scores (p = 0.002) and subscores (difficulty identifying and describing feelings; all p ≤ 0.026). Five patients (14.3%) reached the threshold for alexithymia and 6 (17.1%) for possible alexithymia. No HC scored positive for alexithymia and only 2 (11.8%) did for possible alexithymia. TAS-20 score correlated inversely with emotion recognition task performance (r = −0.411; p = 0.014). Interpretation We found poorer performance in emotion recognition in CD patients compared to HC. Together with a different gaze pattern and higher scores for alexithymia our results highlight deficits in emotion processing in CD.
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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 | 0.000 | 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".