Levodopa Dose Equivalency in Parkinson's Disease: Updated Systematic Review and Proposals
Bibliographic record
Abstract
BACKGROUND: To compare drug regimens across clinical trials in Parkinson's disease (PD) conversion formulae between antiparkinsonian drugs have been developed. These are reported in relation to levodopa as the benchmark drug in PD pharmacotherapy as 'levodopa equivalent dose' (LED). Currently, the LED conversion formulae proposed in 2010 by Tomlinson et al. based on a systematic review are predominantly used. However, new drugs with established and novel mechanisms of action and novel formulations of longstanding drugs have been developed since 2010. Therefore, consensus proposals for updated LED conversion formulae are needed. OBJECTIVES: To update LED conversion formulae based on a systematic review. METHODS: The MEDLINE, CENTRAL, and Embase databases were searched from January 2010 to July 2021. Additionally, in a standardized process according to the GRADE grid method, consensus proposals were issued for drugs with scarce data on levodopa dose equivalency. RESULTS: The systematic database search yielded 3076 articles of which 682 were eligible for inclusion in the systematic review. Based on these data and the standardized consensus process, we present proposals for LED conversion formulae for a wide range of drugs that are currently available for the pharmacotherapy of PD or are expected to be introduced soon. CONCLUSIONS: The LED conversion formulae issued in this Position Paper will serve as a research tool to compare the equivalence of antiparkinsonian medication across PD study cohorts and facilitate research on the clinical efficacy of pharmacological and surgical treatments as well as other non-pharmacological interventions in PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of 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 | 0.092 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".