Pseudobulbar Affect Correlates With Mood Symptoms in Parkinsonian Disorders but Not Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Pseudobulbar affect (PBA) is a syndrome of affective disturbance associated with inappropriate laughter and crying, independent of mood. PBA is common in amyotrophic lateral sclerosis (ALS) and increasingly recognized in Parkinson's disease (PD) and atypical parkinsonism (aP). Correlates of PBA have not been systematically studied. The purpose of this study was to determine whether cognitive and psychiatric comorbidities correlated with patient-reported symptoms of PBA by using the Center for Neurological Study-Lability Scale among patients with ALS, PD, and aP. A total of 108 patients (PD, N=53; aP, N=29; ALS, N=26) completed a cognitive screener and self-reported measures of lability, depression, anxiety, apathy, and quality of life. Statistical analyses included one- and two-way analyses of covariance to evaluate group differences, Pearson's correlations to determine relationships between PBA symptoms and comorbidities, multiple regression for predicting PBA symptom severity in clinical correlates, and chi-square t tests for predicting demographic variables. PBA symptom severity did not vary between the three groups. Younger age and worse anxiety correlated with PBA symptom severity in all three groups, whereas depression and poor mental health/quality of life only correlated with PBA symptom severity in the PD and aP groups. PD and aP patients may be more likely to benefit from treatment with antidepressants. Increased PBA symptoms were associated with declines in cognitive functioning in the aP group, but sufficient numbers of PD and ALS patients with cognitive dysfunction may not have been recruited. The results suggest the possibility of an alternate pathophysiologic mechanism for PBA, which may vary between neurological disorders and disease progression. Mood and cognition are of particular relevance and should be evaluated when symptoms of PBA are suspected.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".