A scoping review of the role of managed entry agreements in upcoming drugs for amyotrophic lateral sclerosis: learning from the case of spinal muscular atrophy
Bibliographic record
Abstract
The therapeutic options for spinal muscular atrophy (SMA) are encouraging. However, there is currently no cure for amyotrophic lateral sclerosis (ALS). The clinical and economic uncertainty surrounding innovative treatments for rare neurodegenerative diseases makes it necessary to understand managed entry agreements (MEAs). The aim of this study was to review whether models of MEAs in SMA could be extrapolated to ALS. We performed a scoping review with information on MEAs on SMA in Web of Science (WOS), PubMed, Lyfegen Library, the National Institute for Health and Care Excellence (NICE), and the Canadian Agency for Drugs and Technologies in Health (CADTH). We found 45 results in WOS and PubMed. After an initial survey, 10 were reviewed to assess eligibility, and three were selected. We obtained 44 results from Lyfegen Library, and three results each from NICE and CADTH. The main objective of MEAs is to reduce uncertainty in the financing of drugs with a high budgetary impact and clinical concerns, as is the case with drugs for SMA and ALS. While the information available on MEAs in SMA is scarce, some conceptual models are publicly available. MEAs for long-term treatments for SMA could be used for the design of MEAs in ALS because of their similarities in economic and clinical uncertainty.
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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 | 0.063 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".