Risk factors of levodopa-induced dyskinesia in Parkinson’s disease: results from the PPMI cohort
Bibliographic record
Abstract
Levodopa-induced dyskinesias (LID) negatively impact on the quality of life of patients with Parkinson's disease (PD). We assessed the risk factors for LID in a cohort of de-novo PD patients enrolled in the Parkinson's Progression Markers Initiative (PPMI). This retrospective cohort study included all PD patients enrolled in the PPMI cohort. Main outcome was the incidence rate of dyskinesia, defined as the first time the patient reported a non-zero score in the item "Time spent with dyskinesia" of the MDS-UPDRS part IV. Predictive value for LID development was assessed for clinical and demographical features, dopamine transporter imaging (DaTscan) pattern, cerebrospinal fluid (CSF) biomarkers (Aβ42, total tau, phosphorylated tau, total α synuclein) and genetic risk score for PD. Overall, data from 423 PD patients were analyzed. The cumulative incidence rate of LID was 27.4% (95% CI = 23.2-32.0%), with a mean onset time of 5.81 years from PD diagnosis. Multivariate Cox regression analysis showed several factors predicting LID development, including female gender (HR = 1.61, 95% CI = 1.05-2.47), being not completely functional independent as measured by the modified Schwab & England ADL scale (HR = 1.81, 95% CI = 0.98-3.38), higher MDS-UPDRS part III score (HR = 1.03, 95% CI = 1.00-1.05), postural instability gait disturbances or intermediate phenotypes (HR = 1.95, 95% CI = 1.28-2.96), higher DaTscan caudate asymmetry index (HR = 1.02, 95% CI = 1.00-1.03), higher polygenic genetic risk score (HR = 1.39, 95% CI = 1.08-1.78), and an anxiety trait (HR = 1.02, 95% CI = 1.00-1.04). In PD patients, cumulative levodopa exposure, female gender, severity of motor and functional impairment, non-tremor dominant clinical phenotype, genetic risk score, anxiety, and marked caudate asymmetric pattern at DaTscan at baseline represent independent risk factors for developing LID.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".