Delineation of the cognitive and neuropsychiatric features of Pisa syndrome in dementia with Lewy bodies
Bibliographic record
Abstract
Background Pisa syndrome (PS), characterized by trunk lateral flexion, remains rarely reported in dementia with Lewy bodies (DLB). While its pathogenesis is multifactorial, underlying mechanisms are not fully understood. Given the established link between postural control and cognition in aging populations, cognitive impairment has been implicated in PS development. Notably, no studies have investigated the potential contributions of cognitive dysfunction and neuropsychiatric symptoms to the development of PS in DLB patients or its relationship with caregiver burden. Methods This study included 35 DLB patients with PS and 183 DLB patients without PS. We compared cognitive function across different domains using the Montreal Cognitive Assessment (MoCA) and its subdomains, and the Clock Drawing Test (CDT), and dementia severity using the Clinical Dementia Rating (CDR). The prevalence and severity of neuropsychiatric symptoms and caregiver distress were measured using the Neuropsychiatric Inventory (NPI) and Zarit Burden Interview (ZBI). Results The patients in the PS group showed significantly worse performances in attention ( p = 0.012), visuospatial/executive abilities ( p = 0.013), and lower scores on the CDT ( p = 0.007). The PS group demonstrated significantly elevated NPI total scores ( p = 0.008), with higher frequency and severity of delusions ( p = 0.006 and p = 0.008), hallucinations ( p = 0.004 and p < 0.001), and aberrant motor behaviors ( p = 0.020 and p = 0.006). The PS group also had significantly higher ZBI scores ( p = 0.023). Caregivers in the PS group reported greater distress and burden related to delusion ( p = 0.030), hallucination ( p < 0.001), anxiety ( p = 0.020), aberrant motor behavior ( p = 0.001), and sleep disturbance ( p = 0.009). Conclusion Our study reveals that PS in DLB is associated with specific deficits in attention and visuospatial/executive function, alongside more severe neuropsychiatric symptoms. These findings highlight the need for comprehensive management targeting both postural control and neuropsychiatric issues to alleviate caregiver burden. Future longitudinal studies are warranted to clarify the causal nature of these relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".