Rett syndrome in Ireland: a demographic study
Bibliographic record
Abstract
BACKGROUND: Rett syndrome (RTT) is a rare neurodevelopmental condition associated with mutations in the gene coding for the methyl-CpG-binding protein 2 (MECP2). It is primarily observed in girls and affects individuals globally. The understanding of the neurobiology of RTT and patient management has been improved by studies that describe the demographic and clinical presentation of individuals with RTT. However, in Ireland, there is a scarcity of data regarding individuals with RTT, which impedes the ability to fully characterize the Irish RTT population. Together with the Rett Syndrome Association of Ireland (RSAI), we prepared a questionnaire to determine the characteristics of RTT individuals in Ireland. Twenty-five families have participated in the study to date, providing information about demographics, genetics, familial history, clinical features, and regression. RESULTS: The results show that Irish individuals with RTT have comparable presentation with respect to individuals in other countries; however, they had a better response to anti-epileptic drugs, and fewer skeletal deformities were reported. Nonetheless, seizures, involuntary movements and regression were more frequently observed in Irish individuals. One of the main findings of this study is the limited genetic information available to individuals to support the clinical diagnosis of RTT. CONCLUSIONS: Despite the limited sample size, this study is the first to characterize the RTT population in Ireland and highlights the importance of having a swift access to genetic testing to sharpen the characterization of the phenotype and increase the visibility of Irish individuals in the international RTT community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".