The Impact of Upcoming Treatments in Huntington’s Disease: Resource Capacity Limitations and Access to Care Implications
Bibliographic record
Abstract
BACKGROUND: The most advanced disease-modifying therapies (DMTs) in development for Huntington's disease (HD) require intrathecal (IT) administration, which may create or exacerbate bottlenecks in resource capacity. OBJECTIVE: To understand the readiness of healthcare systems for intrathecally administered HD DMTs in terms of resource capacity dynamics and implications for patients' access to treatment. METHODS: Forty HD centres across 12 countries were included. Qualitative and quantitative data on current capacity in HD centres and anticipated capacity needs following availability of a DMT were gathered via interviews with healthcare professionals (HCPs). Data modelling was used to estimate the current capacity gap in HD centres. RESULTS: From interviews with 218 HCPs, 25% of HD centres are estimated to have the three components required for IT administration (proceduralists, nurses and facilities). On average, 114 patients per centre per year are anticipated to receive intrathecally administered DMTs in the future. At current capacity, six of the sampled centres are estimated to be able to deliver DMTs to all the anticipated patients based on current resources. The estimated waiting time for IT administration at current capacity will average 60 months (5 years) by the second year after DMT availability. CONCLUSION: Additional resources are needed in HD centres for future DMTs to be accessible to all anticipated patients. Timely collaboration by the HD community will be needed to address capacity gaps. Healthcare policymakers and payers will need to address costs and navigate challenges arising from country- or region-specific healthcare delivery schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".