Pisa syndrome in Parkinson's disease: An integrated approach from pathophysiology to management
Bibliographic record
Abstract
Pisa syndrome was first described in 1972 in patients treated with neuroleptics. Since 2003, when it was first reported in patients with Parkinson's disease (PD), Pisa syndrome has progressively drawn the attention of clinicians and researchers. Although emerging evidence has partially clarified its prevalence and pathophysiology, the current debate revolves around diagnostic criteria and assessment and the effectiveness of pharmacological, surgical, and rehabilitative approaches. Contrary to initial thought, Pisa syndrome is common among PD patients, with an estimated prevalence of 8.8% according to a large survey. Furthermore, it is associated with the following specific patient features: more severe motor phenotype, ongoing combined pharmacological treatment with levodopa and dopamine agonists, gait disorders, and such comorbidities as osteoporosis and arthrosis. The present literature on treatment outcomes is scant, and the uneven effectiveness of specific treatments has produced conflicting results. This might be because of the limited knowledge of Pisa syndrome pathophysiology and its variable clinical presentation, which further complicates designing randomized clinical trials on this condition. However, because some forms of Pisa syndrome are potentially reversible, there is growing consensus on the importance of its early recognition and the importance of pharmacological adjustment and rehabilitation. © 2016 International Parkinson and Movement Disorder Society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".