Levodopa Equivalent Daily Dosage: Geographical Variations and Real‐Life Modules in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: The Levodopa Equivalent Daily Dosage (LEDD) calculation algorithms help in capturing and harmonization of Parkinson's Disease (PD) therapies. Analyzing these updates is essential for validating their effectiveness. OBJECTIVE: To assess updated LEDD conversion factors in capturing the newer therapies in PD and therapy modules in different geographical cohorts. METHODS: Data were sourced from 10 Centers from 6 countries representing 2 different continents. The study compared the LEDD conversion factors proposed by Tomlinson et al and Jost et al, alongside investigating demographic disparities. RESULTS: The analysis involved 2943 subjects; 87% (n = 2577) met the UK Brain Bank criteria for PD. The LEDD differed significantly across methodologies (Tomlinson vs. Jost, 598 mg vs 610 mg, P < 0.0001). Geographical disparities highlighted variations in PD onset age (P < 0.0001). Jost and Tomlinson's calculations demonstrated consistency within but significant differences across countries (P < 0.0001).Age at onset revealed statistically significant differences in LEDD requirements (P < 0.0001), which were particularly higher in 21-50 years (718 mg vs 566 mg). This subgroup also demonstrated increased usage of non-Levodopa therapies (P < 0.0001). Men exhibited higher total LEDD (P = 0.001). 34% reported dyskinesia, associated with higher LEDD (756 mg, P < 0.0001). Surgically treated patients also had higher LEDD (P < 0.0001) and a significant difference between Jost and Tomlinson dosages (761 mg vs716mg) reflecting the incorporation of newer therapeutic molecules. CONCLUSION: This analysis delineates the importance of updated LEDD algorithms and intricacies in the landscape of PD treatment, underscored by geographical, age-related, and gender-specific variations, in real-life management scenarios.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".