Foslevodopa/foscarbidopa (LDp/CDp) in advanced Parkinson’s disease (aPD): demonstration of savings from a societal perspective in the UK
Bibliographic record
Abstract
AIMS: In advanced Parkinson's disease (aPD), adequate 24-hour control of OFF-time may not be achievable using oral/transdermal therapies. Clinical trials of foslevodopa/foscarbidopa (LDp/CDP) demonstrate meaningful reductions in OFF-time and OFF-related sleep disturbance in aPD. Previous analyses have only considered direct medical costs: this analysis considers a broader societal perspective (direct non-medical costs, informal care, loss of earnings, productivity, and tax). METHODS: Inputs for the societal impact model were taken from a cost-utility model comparing LDp/CDp with best medical treatment (BMT), accepted by the UK National Institute of Health and Care Excellence (NICE). Quintiles of normalized OFF-time across a 16-hour waking day in each treatment group were applied to literature-based estimates for direct medical, non-medical, and indirect costs. The resulting state-specific cost estimates were applied to the modelled aPD patient population. RESULTS: The model estimates the potential UK population for LDp/CDp at 17,505. Continuous 24-hour delivery of LDp/CDp results in greater time spent in OFF-time states 0-1 (0-4 h of OFF-time/16-hour waking day) vs BMT alone. Net costs in year 1, if all eligible patients receive LDp/CDp, are £45.5 M. Cumulative net savings thereafter are £47.0 M by year 2, rising to £166.1 M by year 3, £261.9 M by year 4 and £312.2 M by year 5. Results are robust in scenario analyses (excluding costs of sleep disturbance, earnings loss, productivity, and tax loss). LIMITATIONS: A NICE-accepted model was used as the economic modelling basis for the societal impact model. However, much of the data was derived from Adelphi datasets, with the potential for inconsistent definitions. CONCLUSION: When considered from a societal perspective, the use of LDp/CDp in aPD patients inadequately controlled on oral therapy, is associated with a reduction in medium-term healthcare and societal costs vs BMT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".