232 Organisational impact of upcoming Huntington’s disease treatments in Europe: resource gaps, access to care
Bibliographic record
Abstract
Whilst no disease-modifying-therapies (DMTs) are currently available for Huntington’s disease (HD), the investigational drugs most advanced in clinical development are administered intrathecally, requiring additional resources in HD clinics. We investigated the impact of upcoming DMTs for HD on healthcare systems and the implications of possible resource capacity gaps on access to care. The capacity to perform intrathecal drug administrations was assessed in 35 HD specialist centres from nine countries. Interviews with >170 healthcare professionals were performed and resources available in each HD centre were compared to the predicted amount of future resources that the estimated eligible patient population would need. Only 20% of participating HD teams currently have the required resources to perform intrathecal injec- tions: a skilled ‘proceduralist’, one or more nurses to assist in the procedure, and the appropriate space. When considering all resources available in the hospital, only 17% of HD-specialist clinics are estimated to have enough capacity to serve the eligible population. When simulating the additional referral-in of patients from non-HD-specialised clinics, only 6% of HD clinics have enough capacity. To ensure adequate care, capacity-constrained healthcare systems will need to plan adequately and ensure providers have sufficient training and resources to deliver new intrathecally administered DMTs. kopano.mukelabai@roche.com
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".