Pisa Syndrome in Chinese Patients With Parkinson's Disease
Bibliographic record
Abstract
Objective: To investigate the prevalence and the clinical factors related to Pisa syndrome (PS) in Chinese Parkinson's disease (PD) patients. Methods: A total of 2167 PD patients were continuously included in this observational study. Patients with PS were identified as presented with a lateral trunk flexion of at least 10° that can be completely alleviated by passive mobilization or supine positioning. The data of the motor and non-motor symptoms including depression, anxiety and cognitive dysfunction was collected and analyzed. Results: We found seventy-seven (3.6%) PD patients presented with PS. The following variables including age, disease duration, levodopa equivalent daily doses (LEDD), the proportion of males, the proportion of participants using levodopa, dopaminergic agonist, amantadine and entacapone, the proportion of motor fluctuations, scores of Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Unified PD Rating Scale (UPDRS) part III and Hoehn and Yahr (H&Y) stage were significantly higher in patients with PS compared with patients without PS (P < 0.05). Scores of the Frontal Assessment Battery (FAB) and the Montreal Cognitive Assessment (MoCA) were not different between the two groups. The binary logistic regression model indicated that the presence of PS was associated with older age (OR=1.027, P = 0.030), higher LEDD (OR=1.002, P < 0.001) and a higher UPDRS III score (OR=1.060, P < 0.001), but had no relationship with HAMD and HAMA scores. Conclusion: PS is relatively rare (3.6%) in Chinese PD patients. It is likely associated with older age, higher LEDD and more severe motor disabilities. However, non-motor symptoms such as depression, anxiety and cognitive dysfunction have no association with PS in PD. These findings provided important complementary information for identifying the underlying mechanisms of PS.
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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 | 0.001 |
| 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".