High occurrence of impaired emotion recognition after ischemic stroke
Bibliographic record
Abstract
Abstract Introduction Deficits of emotion recognition after ischemic stroke are often overlooked by clinicians, and are mostly not spontaneously reported by patients. However, impaired emotion recognition after stroke negatively affects the ability to return to work and the quality of life. It is still unknown how often impairments of emotion recognition occur shortly after ischemic stroke. We aimed to estimate the occurrence of impaired emotion recognition after ischemic stroke and to characterise these patients with impaired emotion recognition. Patients and methods Two hundred thirty patients were included, derived from a prospective study of cognitive recovery. Five weeks after ischemic stroke a neuropsychological assessment was performed, including an emotion recognition task (i.e. Ekman 60-faces test). Emotion recognition was regarded as impaired if the total score was below the fifth percentile for a large independent reference sample. Results Emotion recognition was impaired in 33.5% of patients. Patients with impaired emotion recognition were more likely to have an abnormal Montreal Cognitive Assessment during hospitalisation, and 5 weeks after their stroke, a higher proportion of them had a vascular cognitive disorder (VCD). Even 20% of patients without VCD had impaired emotion recognition. Discussion: Emotion recognition was often impaired after ischemic stroke. This is clinically relevant, since impaired emotion recognition negatively impacts social functioning. Conclusion: Even when there was no cognitive disorder in traditional cognitive domains, emotion recognition was impaired in 1 out of 5 patients. Clinicians should systematically ask patients and their caregivers about deficits in emotion recognition, and, if needed, test for these deficits.
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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 | 0.002 |
| 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 | 0.001 | 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".