An <scp>MDS</scp> Evidence‐Based Review on Treatments for Huntington's Disease
Bibliographic record
Abstract
BACKGROUND: Huntington's disease (HD) is a rare neurodegenerative disorder with protean clinical manifestations. Its management is challenging, consisting mainly of off-label treatments. OBJECTIVES: The International Parkinson and Movement Disorder Society commissioned a task force to review and evaluate the evidence of available therapies for HD gene expansion carriers. METHODS: We followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Eligible randomized controlled trials were identified via an electronic search of the CENTRAL, MEDLINE, and EMBASE databases. All eligible trials that evaluated one or more of 33 predetermined clinical questions were included. Risk of bias was evaluated using the Cochrane Risk of Bias tool. A framework was adapted to allow for efficacy and safety conclusions to be drawn from the balance between the GRADE level of evidence and the importance of the benefit/harm of the intervention. RESULTS: Twenty-two eligible studies involving 17 interventions were included, providing data to address 8 clinical questions. These data supported a likely effect of deutetrabenazine on motor impairment, chorea, and dystonia and of tetrabenazine on chorea. The data did not support a disease-modifying effect for premanifest and manifest HD. There was no eligible evidence to support the use of specific treatments for depression, psychosis, irritability, apathy, or suicidality. Similarly, no evidence was eligible to support the use of physiotherapy, occupational therapy, exercise, dietary, or surgical treatments. CONCLUSIONS: Data for therapeutic interventions in HD are limited and support only the use of VMAT2 inhibitors for specific motor symptoms. © 2021 International Parkinson and Movement Disorder Society.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".