The epidemiology and pathophysiology of pseudobulbar affect and its association with neurodegeneration
Bibliographic record
Abstract
Rebecca R King, Jeffrey P ReissDepartment of Psychiatry, University of Western Ontario, London Health Sciences Centre, Victoria Hospital, London, Ontario, CanadaAbstract: Pseudobulbar affect is a disorder resulting from neurologic damage manifesting as sudden, stereotyped affective outbursts that are not reflective of internal emotion. A literature review was completed to examine the current understanding of the epidemiology, characterization, diagnosis, pathophysiology, and treatment of pseudobulbar affect. This review revealed that it is common in neurodegenerative disorders but is poorly recognized, placing significant impacts on patients and their families. The disorder appears to result from a disruption of the cortico-limbic-subcortical-thalamic-pontocerebellar network involved in emotional expression and regulation with resulting disruptions of neurotransmitter systems. Effective treatment is available with agents such as selective serotonin reuptake inhibitors and dextromethorphan combined with quinidine, but further well-designed comparative studies are needed. Advances in technology such as neuroimaging may enhance knowledge about the pathophysiology of this disorder, and help guide future interventions.Keywords: pseudobulbar affect, pathological laughing and crying, neurodegenerative disease, pathophysiology, epidemiology, treatment
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".