Gender and Dopaminergic Medication Treatment in Early Parkinson Disease (P3.069)
Bibliographic record
Abstract
OBJECTIVE: To test if there are differences in the type of dopaminergic treatment and levodopa equivalent daily dose (LEDD) between men and women with early Parkinson Disease (PD) in a large multicenter study- the National Institute of Neurological Disorders and Stroke Exploratory Trials in Parkinson Disease (NINDS NET-PD) Long-Term Study-1 (LS-1). BACKGROUND: There are limited studies examining dopaminergic medication treatment in men versus women with early PD. Knowledge of differential exposure to dopaminergic treatments according to gender could inform future clinical management. DESIGN/METHODS: Baseline data of 1,741 participants from 45 participating sites were analyzed. Participants from the United States and Canada were enrolled within five years of PD diagnosis. Two outcome variables were studied: type of dopaminergic treatment and LEDD (mg) at enrollment in LS-1. Chi-square statistic and linear regression models were used for statistical analysis. RESULTS: There were no statistically significant differences in the frequency of use of different types of dopaminergic treatments at enrollment between men and women. A small but statistically significant difference was observed in the median unadjusted LEDD at baseline between women (300mg) and men (325mg), but this was not observed after controlling for type of dopaminergic therapy, years since PD diagnosis and body weight. CONCLUSIONS: Although we did not observe gender differences in the type and dose of dopaminergic treatment in LS-1 participants with early PD, further research is needed to evaluate gender influences on dopaminergic medication treatment in mid- and late-stage PD. Study Supported by: on behalf of the NET-PD LS1 investigators.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".