J13 Prescription usage for treatment of irritability, perseverative behaviors, and chorea in huntington's disease
Bibliographic record
Abstract
Background Despite large gaps in evidence-based knowledge, clinical experience supports the use of pharmacologic treatments for many symptoms of Huntington9s disease (HD). Aims The project goal is to develop consensus guidelines based on expert clinical experience to improve quality of HD care. Methods The survey was developed by 9 international experts, and designed as a highly iterative and systematic method of soliciting expert opinions on the pharmacologic treatment of irritability, perseverative behaviors, and chorea in HD. Fifty-five experts from Australia, Europe and North America responded to at least one of the surveys. Results For irritability, SSRI was first choice of 58%, an antipsychotic was first choice for 22%, a mood stabilizing anticonvulsant 14%, and benzodiazepine 2%. For perseverative behaviors, SSRI was first choice of 75%, an antipsychotic choice of 4%, a mood stabilizing anticonvulsant choice of 6%, and clomipramine 2%. The remaining 13% chose to qualify the response to include 2 first choices. For chorea, an antipsychotic was first choice for 56%, tetrabenazine 15%, amantadine 6%, and benzodiazepines 4%. The remaining 13% chose to qualify the response to include 2 first choices. Drug choice for use as adjunctive therapy was widely variable. Conclusions Many areas of variability in treatment for irritability, perseverative behaviors, and chorea in HD, have been identified. Results will guide future rounds of the Delphi process to elicit rational causes for differences identified. When complete, this process will clarify useful treatment paradigms that will improve patient care and identify areas in which clinical trials would be most useful.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".