Postural Abnormalities in Parkinson's Disease: An Epidemiological and Clinical Multicenter Study
Bibliographic record
Abstract
ABSTRACT Introduction The overall frequency of postural abnormalities (PA) in Parkinson's disease (PD) is unknown. We evaluated the overall prevalence of PA and assessed the association with demographic and clinical variables. Methods For this multicenter, cross‐sectional study, consecutive PD outpatients attending 7 tertiary Italian centers were enrolled. Patients were evaluated and compared for the presence of isolated PA such as camptocormia, Pisa syndrome, and anterocollis and for combined forms (ie, camptocormia + Pisa syndrome) together with demographic and clinical variables. Results Of the total 811 PD patients enrolled, 174 (21.5%; 95% confidence interval [CI], 18.6%–24.3%) presented PA, 144 of which had isolated PA and 30 had combined PA. The prevalence of camptocormia was 11.2% (95% CI, 9%–13.3%), Pisa syndrome 8% (95% CI, 6.2%–9.9%), and anterocollis 6.5% (95% CI, 4.9%–8.3%). Patients with PA were more often male and older with longer disease duration, more advanced disease stage, more severe PD symptoms, a bradykinetic/rigid phenotype, and poorer quality of life. They were initially treated with levodopa, and more likely to be treated with a combination of levodopa and dopamine agonist, took a higher daily levodopa equivalent daily dose, and had more comorbidities. Falls and back pain were more frequent in PD patients with PA than in those without PA. Multiple logistic regression models confirmed an association between PA and male gender, older age, Hoehn and Yahr stage, and total Unified Parkinson's Disease Rating Scale score. Conclusions PA are frequent and disabling complications in PD, especially in the advanced disease stages.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".