P.081 Real-world benefits and tolerability of trofinetide for the treatment of Rett Syndrom: interim analysis of the LOTUS study
Bibliographic record
Abstract
Background: Trofinetide is approved for the treatment of Rett syndrome (RTT) in patients aged ≥2 years. Here, we present the benefits and tolerability of trofinetide in the treatment of RTT with the 12-month follow-up of LOTUS. Methods: Caregivers of patients who are prescribed trofinetide under routine clinical care are eligible to participate. Assessments include the Behavioral Improvement Questionnaire (BIQ), the Quality-of-Life Inventory-Disability (QI-Disability) Questionnaire, and the Gastrointestinal Health Questionnaire. Due to ongoing enrollment, data are reported to 9 months since the initiation of trofinetide. Results: In total, 192 patients were included. The median dose reported at week 1 was 45.0% of the target weight-banded label dose; by week 9 onwards, the median dose was at least 80.0% of the target weight-banded label dose. Behavioral improvements reported with the BIQ were nonverbal communication (49–62%), alertness (43–62%), and social interaction/connectedness (32–52%). The QI-Disability Questionnaire median total scores indicated overall improvement in quality of life (QoL) with trofinetide. Caregivers reported that patients were most likely to void normal stools over the follow-up; most reports of diarrhea were contained inside the patient’s diaper. Conclusions: Caregivers of patients with RTT in LOTUS reported behavioral improvements of RTT symptoms and improvement in patients’ QoL.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".