Correlation between cognitive impairment during the acute phase of first cerebral infarction and development of long-term pseudobulbar affect
Bibliographic record
Abstract
Purpose: The relationship between cognitive impairment during the acute phase of first cerebral infarction and the development of long-term pseudobulbar affect (PBA) has not been elucidated. Therefore, in this study, we aimed to determine if cognitive impairment during the acute phase of cerebral infarction will increase the risk of long-term post-infarction PBA. Patients and methods: This was a nested case–control study using a prospective approach. A consecutive multicenter matched 1:1 case–control study of cognitive impairment cases following acute cerebral infarction (N=26) with 26 sex-, education years-, and age-matched controls. Univariate and multivariate conditional logistic regression analyses were performed to study the clinical features and changes in cognitive domain as well as the risk factors for PBA. Results: Long-term PBA was independently predicted by low Montreal cognitive assessment (MoCA) scores at baseline. Multivariable regression models showed that post-infarction low MoCA scores remained independent predictors of long-term PBA (odds ratio [OR]=0.72; 95% confidence interval [CI]=0.54–0.95; P =0.018). Among all cognitive disorders, digit span test (DST) scores (OR=0.39; 95% CI=0.16–0.91, P =0.030), StroopC time (OR=1.15; 95% CI=1.01–1.31; P =0.037), and clock-drawing task (CDT) scores (OR=0.62; 95% CI=0.42–0.90; P =0.013) were found to be the independent risk factors for PBA. Conclusion: Cognitive impairment during the acute phase of cerebral infarction increased the risk of cerebral infarction-induced long-term PBA. Development of PBA was closely associated with executive function, attention, and visuospatial disorder. Keywords: cognitive impairment, pseudobulbar affect, cerebral infarction, neuropsychological tests, cognitive domain
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".