Alexithymic Features in Stroke: Effects of Laterality and Gender
Bibliographic record
Abstract
OBJECTIVE: Stroke patients suffer from a high rate of behavioral disorders, and the laterality of the lesion may affect the expression of emotional disturbances. This study tested the hypothesis that stroke patients with a lesion in the right hemisphere are at high risk of developing alexithymic features. METHODS: Forty-eight patients were interviewed with the Structured Clinical Interview for DSM-IV (patient edition), the Mini-Mental State Examination, the State-Trait Anxiety Inventory (state form), the Beck Depression Inventory, and the Toronto Alexithymia Scale (20-item version). Alexithymic differences between stroke patients with a lesion in the right hemisphere and those with a lesion in the left hemisphere were computed by analysis of covariance, using scores on the Mini-Mental State Examination, Beck Depression Inventory (psychic subscore), and State-Trait Anxiety Inventory as covariates and the score on the Toronto Alexithymia Scale as the dependent variable. A multivariate analysis of covariance and a series of follow-up analyses of covariance with the same covariates were used to discriminate differences in subscores on the Toronto Alexithymia Scale. An exploratory analysis of covariance was also performed to determine the effect of gender on alexithymic features in both groups of stroke patients. RESULTS: The 21 stroke patients with a lesion in the right hemisphere were more alexithymic than the 27 patients with a lesion in the left hemisphere. This evidence was strengthened by the categorical analysis: 48% of the patients with a right-hemisphere lesion had alexithymia, compared with 22% of patients with a left-hemisphere lesion. Univariate analyses of covariance showed significant differences between the two groups in difficulty identifying feelings and difficulty describing feelings, but not in externally oriented thinking. The last exploratory analysis of covariance suggested that gender may influence alexithymic features. CONCLUSIONS: This study provides direct evidence that alexithymia, and more specifically difficulty identifying feelings and difficulty describing feelings, is more common in stroke patients with a right-hemisphere lesion than in those with a left-hemisphere lesion. It also provides preliminary evidence that gender may affect alexithymic expression.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".