Real-world use of trofinetide: a survey of practices from prescribers at Rett syndrome centers of excellence in the United States
Bibliographic record
Abstract
Introduction Trofinetide is approved for the treatment of Rett syndrome (RTT) in the United States and Canada. The objective of this study was to describe RTT expert approach to trofinetide titration and observed impact in their practice.Methods In May 2024, an electronic survey was sent to prescribers at RTT centers of excellence (COEs) in the United States to collect real-world experience with trofinetide dosing strategies and observed impact on tolerability.Results Overall, 67% (22/33) of prescribers from 89% (16/18) of COEs responded to the survey. Most respondents (95%, n = 21) indicated they titrate trofinetide and 86% (n = 19) believed titration improved tolerability. Titration strategies included initiating at a lower percentage (50%, n = 11) or initiating at lower milliliters (27%, n = 6) than label dose, or individualized starting dose based on baseline conditions (14%, n = 3). Respondents estimated a median 75% of patients achieve their label dose after titration. Respondents encourage families to remain on trofinetide for a median of ≥16 weeks to adequately evaluate efficacy; most (77%, n = 17) indicated this period should start after the patient arrives at their highest tolerable dose.Conclusions Survey respondents from RTT COEs in the United States are most commonly titrating trofinetide to improve tolerability while decreasing treatment discontinuations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".