Rehabilitation interventions in Rett syndrome: a scoping review
Bibliographic record
Abstract
Aim To summarize existing interventions and their outcomes in Rett syndrome (RTT) rehabilitation and identify gaps in the literature. Method Five databases (Ovid MEDLINE, Ovid Embase Classic, Ovid PsycINFO, EBSCO CINAHL Plus, and ProQuest ERIC) were systematically searched up to 23rd July 2018 for studies describing rehabilitation interventions. Data on study participants, design, and outcomes were extracted. Results Sixty‐two articles were included in the final review. Evidence consistently demonstrated that females with RTT can improve their gross motor, fine motor, and communicative skills with rehabilitation. All 11 interventions targeting gross motor function, namely ambulation, achieved functional improvements. Twenty of 24 articles describing fine motor rehabilitation studies succeeded in decreasing stereotypies, improving functional hand use, and/or reducing self‐injurious behaviors. Twenty‐one of 22 studies describing communication interventions succeeded in training choice‐making, communicative language, or socialization behavior. Other key findings include the positive interplay between physical and communicative rehabilitation outcomes, and the ability of females with RTT to improve their cognitive abilities during intervention. Interpretation Rehabilitation can impact the daily lives of females with RTT and their caregivers in clinically meaningful ways. What this paper adds Females with Rett syndrome (RTT) can make developmental gains across their life span. Long‐term intervention can improve the physical abilities of individuals with RTT. Low‐, medium‐, and high‐technology interventions can improve the communicative abilities of individuals with RTT.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".