Epidemiology of Huntington disease in Cyprus: A 20‐year retrospective study
Bibliographic record
Abstract
Huntington disease (HD) is most prevalent among populations of western European descent and isolated populations where founder effects may operate. The aim of this study was to examine the epidemiology of HD in Cyprus, an island in southern Europe with extensive western European colonization in the past. All registered HD patients in the Cyprus, since 1994, were included. Detailed pedigrees and clinical information were recorded and maps, showing the geographic distribution of HD, were constructed. Requests for genetic testing were also examined. The project identified 58 clinically manifested cases of HD belonging to 19 families. The 16 families of Cypriot origin were concentrated in a confined geographical cluster in southeast Cyprus. In 2015, prevalence of symptomatic HD was 4.64/100 000 population, while incidence was 0.12/100 000 person-years. Prevalence displayed a marked increase during the past 20 years. Disease characteristics of HD patients were similar to those reported in western European populations. Lastly, the uptake of predictive and/or prenatal testing was limited. HD disease characteristics, incidence and prevalence in Cyprus were comparable to western European populations. Together with the geographical clustering observed, these results support the possibility for a relatively recent founder effect of HD in Cyprus, potentially of western European origin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".