Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to review our "real-world" experience with the vesicular monoamine transporter 2 (VMAT2) inhibitors tetrabenazine (TBZ), deutetrabenazine (DTBZ), and valbenazine (VBZ) for treatment of hyperkinetic movement disorders. Access and adherence to VMAT2 inhibitors may be limited by insurance and regulatory issues, inexperience with their use by the prescribing physician, lack of efficacy, or side effects. METHODS: We performed a retrospective chart review, supplemented with a questionnaire, of all our patients treated with a VMAT2 inhibitor between January 1, 2017, and August 30, 2018. RESULTS: We identified 135 patients (57.8% male) and 178 prescriptions for VMAT2 inhibitors (TBZ, n = 45 [25.3%]; DTBZ, n = 104 [58.4%]; VBZ, n = 29 [16.3%]). Tourette syndrome/tics was the most common diagnosis (n = 67 [49.6%]) for which VMAT2 inhibitors were prescribed. The VMAT2 inhibitor mean treatment durations (range; SD) and daily dosages (range; SD) were as follows: TBZ (n = 31), 5.1 months (1-19; 3.9) at 48.8 mg (12.5-112.5; 29.6); DTBZ (n = 51), 8.0 months (0.25-16.5; 4.4) at 34.4 mg (6-96; 20.7); and VBZ (n = 20), 6.0 months (0.1-16; 5.6) at 64 mg (40-160; 35.3). The VMAT2 inhibitors effectively controlled hyperkinetic movement disorders as measured by a 1- to 4-point Likert scale (1 = normal or mildly ill, 4 = severely ill) comparing illness severity before starting and while on treatment (score of 1 in 13.0%-26.7% vs 60.9%-71.9% of patients). Side effects were mild and improved or resolved following dose reduction, drug cessation, or addition of adjunctive medications. CONCLUSIONS: The VMAT2 inhibitors are effective and safe in a range of hyperkinetic movement disorders but are not readily accessible by patients in the United States for indications not approved by the Food and Drug Administration.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".