Prevalence of Fragile X syndrome in Georgian patients with autism spectrum disorder and/or intellectual disability: cross-sectional study and review of current approaches
Bibliographic record
Abstract
Fragile X syndrome (FXS) is the most common inherited form of intellectual disability (ID) and autism spectrum disorder (ASD). Despite its clinical importance, data on FXS prevalence in Georgia remains limited. This study aims to assess the prevalence of FXS in individuals with ID and/or ASD in Georgia and to review current diagnostic and management approaches. A total of 441 patients (n = 332 males and n = 109 females) diagnosed with ID and/or ASD based on DSM-5 criteria underwent genetic testing for FXS using a PCR-based approach. The FXS full mutation was identified in 25 patients (5.7%), and four individuals were carriers of the premutation. One patient had a large FMR1 deletion, thus the prevalence of the full mutation (FM) was 5.9%, and the prevalence of a premutation was 0.9%. The FXS-positive cohort showed a significant male predominance (80.77%). Among patients with ASD, 1.9% tested positive for FXS, and these individuals displayed more severe behavioral problems, requiring more intensive intervention. Phenotypic features such as a long face (76.9%), joint hypermobility (61.5%), and flat feet (53.8%) were commonly observed. The study underscores a significant diagnostic delay, with the average age of clinical ID/ASD diagnosis at 8.42 years and a lag of 4.63 years before FXS is identified. Compared to the U.S., where FXS diagnosis occurs at 35–41 months, Georgia faces significant barriers, including low awareness, lack of early screening, and limited access to genetic testing. Efforts to address these challenges include public awareness campaigns and integration of early genetic testing protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".