The Association of Pathological Laughing and Crying and Cognitive Impairment in Multiple Sclerosis (P2.176)
Bibliographic record
Abstract
Background: Pathological laughing and crying (PLC) is emotional expression that is exaggerated and incongruent with underlying mood state. It is known to occur among 10-29[percnt] of people with Multiple Sclerosis (MS). Among populations with other neurological disorders, PLC has been associated with cognitive impairment (CI). Objective: To determine the association between PLC and CI within an MS sample. Methods: A retrospective chart review study of 153 MS patients recruited from an outpatient clinic for CI in MS. Participants were included if they were assessed with the Minimal Assessment of Cognitive Function in MS (MACFIMS) battery, the Center for Neurological Study Lability Scale (CNS-LS), a screening measure for PLC symptoms and the Hospital Anxiety and Depression Scale (HADS). Exploratory correlations and comparisons between PLC (CNS-LS score ≥ 17 and HADS-D ≤ 7) and non-PLC groups on cognitive test scores were performed. Results: After controlling for covariates, the PLC group demonstrated lower scores on a measure of verbal fluency (Controlled Oral Word Association Test score), and a measure of auditory recall (California Verbal Learning Test - 2 immediate recall score) than the non-PLC group. Conclusions: Findings replicate a deficit in verbal fluency, as well as identifying auditory recall deficits among people with PLC in MS suggesting a generalized verbal processing difficulty. This work is the first to examine the relationship between PLC symptoms and cognition in a large MS sample with a comprehensive cognitive battery. Future studies might examine the causal mechanisms connecting CI and PLC.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".