Assessing the state of care for Huntington disease in the United States: Results from a survey of practices treating Huntington disease patients
Bibliographic record
Abstract
Background: No study to date has thoroughly examined US Huntington disease (HD) care delivery in a variety of clinic settings by HD specialists and non-specialists. Objective: To obtain a clearer understanding of current care structure and delivery of care through a survey of representative US physicians treating HD patients. Methods: We designed and fielded a survey of 40 closed-ended evaluative items and one open-ended item to a sample of 339 US practices. Unique to this survey was the inclusion of non-specialists. Results: Responses were received from 156 practices (overall response rate 46.02 %), with 52.6 % from academic sites, 35.3 % from private practices, and 12.2 % from the VA. More than half (63.5 %) of the practice leads were movement disorder trained or Directors of HDSA Centers of Excellence and 58.3 % had an HD or multidisciplinary care clinic. However, 48.7 % of the practices saw 1-25 HD patients, 28.2 % saw 26-100 HD patients, and 23.1 % served over 100 HD patients annually. Most practices (>69 %) reported having difficulty providing social work, genetic counseling, care coordination and psychologists/psychiatrists. Increased HD practice size was associated with higher rates of pre-visit screenings, care navigator/care coordinators, routine monitoring of weight, and provision of genetic counseling by genetic counselors. Conclusions: Not surprisingly, we found that HD care was inconsistently applied across the US. Practices led by neurologists trained in movement disorders, and higher HD volume practices, tended to be better equipped to provide multi-disciplinary staffing and procedures as compared to those with fewer numbers of HD patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".