Canadian healthcare capacity gaps for disease-modifying treatment in Huntington’s disease: a survey of current practice and modelling of future needs
Bibliographic record
Abstract
OBJECTIVES: Disease-modifying therapies in development for Huntington's disease (HD) may require specialised administration and additional resource capacity. We sought to understand current and future capacity for HD management in Canada considering the possible introduction of an intrathecal (IT) disease-modifying treatment (DMT). DESIGN, SETTING AND PARTICIPANTS: Using a case study, mixed methods framework, online surveys followed by semistructured interviews were conducted in late 2020 and early 2021. Neurologists from Canadian HD (n=16) and community (n=11) centres and social workers (n=16) were invited to complete online surveys assessing current HD management and potential capacity to support administration of an IT DMT. OUTCOME MEASURES: Survey responses, anticipated demand and assumed resource requirements were modelled to reveal capacity to treat (ie, % of eligible patients) by centre. Resource bottlenecks and incremental support required (full-time equivalent, FTE) were also determined. RESULTS: Neurologists from 15/16 HD centres and 5/11 community centres, plus 16/16 social workers participated. HD centres manage 94% of patients with HD currently seeking care in Canada, however, only 20% of IT DMT-eligible patients are currently seen by neurologists. One-third of centres have no access to nursing support. The average national incremental nursing, room, neurologist and social worker support required to provide IT DMT to all eligible patients is 0.73, 0.36, 0.30 and 0.21 FTE per HD centre, respectively. At peak demand, current capacity would support the treatment of 6% of IT DMT-eligible patients. If frequency of administration is halved, capacity for IT-DMT administration only increases to 11%. CONCLUSIONS: In Canada, there is little to no capacity to support the administration of an IT DMT for HD. Current inequitable and inadequate resourcing will require solutions that consider regional gaps and patient needs.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".