Unawareness of motor impairment and emotions in right hemispheric stroke: a preliminary investigation
Bibliographic record
Abstract
BACKGROUND: Awareness may lack in some stroke patients who are not capable of evaluating the nature and severity of illness. Thus, unawareness may have different forms such as anosognosia, neglect, and alexithymia or unawareness of emotions. In this study we investigated the relationship among anosognosia, neglect, alexithymia, and cognition. METHODS: Fifty consecutive right stroke inpatients were approached within the first 3 months from the acute event. Anosognosia was measured with the Bisiach scale, alexithymia with the TAS-20 scale and neglect with line crossing, letter cancellation, figure and shape copying, and line bisection tests. A neuropsychological test battery was used to measure different areas of cognition. RESULTS: despite the strong comorbidity rate among the different forms of unawareness, there are patients who suffer from pure forms of these types of lack of awareness. A multivariate logistic regression model evidenced that presence of neglect (OR = 10.3; 95% CI = 1.4-76.3; p = 0.023) and more difficulty in describing feelings (TAS-20 F2 subscore; OR = 1.3; 95% CI = 1.1-1.7; p = 0.014) were the only predictors of anosognosia. In addition, anosognosics with alexithymia performed worst in a frontal task such as the verbal fluency task (p = 0.042) and in the verbal span forward task (p = 0.026) than pure anosognosics. CONCLUSIONS: Anosognosia for motor impairment is strictly associated with a specific form of unawareness of emotions. Future studies have to clarify if frontal cognitive impairment previously described in anosognosics is a manifestation of unawareness of emotions or anosognosia for motor impairment.
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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".