P.084 Epidemiology and burden of illness in patients with Rett Syndrome in Ontario, Canada
Bibliographic record
Abstract
Background: Rett Syndrome (RTT) is an X-linked neurodevelopmental disorder, characterized by gradual loss of motor, verbal and social skills. This study describes the epidemiology and healthcare resource utilization (HCRU) of RTT in Ontario, Canada. Methods: RTT patients (≥ one ICD-10-CA code F84.2) were identified using data held at the Institute for Clinical Evaluative Sciences (ICES), between September 2018-August 2023. Incidence and prevalence rates from Ontario were extrapolated nationally using the Stats Can population estimates. Results: A total of 246 patients were indexed; 95% female, median age 21 years and 40% from central Ontario. There were 57 incident and 257 prevalent RTT cases identified in Ontario. National extrapolations estimated 175 incident and 613 prevalent RTT cases. Common comorbidities included developmental disability (85.4%) and epilepsy (49.6%). Patients frequently had outpatient visits (primary care 96.7%, specialists 86.6%), emergency department visits (76.8%) and inpatient hospitalizations (54.5%). Most patients (95.1%) had at least one public claim for all-cause medication. Disease-specific medication claims were for anti-infectives (69.1%) and anti-seizure medications associated with mood effects (65.0%). Conclusions: This study provides population-based estimates of RTT in Canada. Findings highlight the high burden of illness and HCRU of RTT and the opportunities to improve healthcare outcomes in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".