The Geographic Diversity of Spinocerebellar Ataxias (SCAs) in the Americas: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The frequency and presentation of each of the most common forms of spinocerebellar ataxias (SCAs) varies widely. In the case of the Americas, this diversity is particularly dynamic given additional social, demographic, and cultural characteristics. OBJECTIVE: To describe the regional prevalence and clinical phenotypes of SCAs throughout the continent. METHODS: A literature search was performed in both MEDLINE and LILACS databases. The research was broadened to include the screening of reference lists of systematic review articles for additional studies. Investigations dating from the earliest available through 2019. Only studies in English, Portuguese, and Spanish were included. We analyzed publications with genetically confirmed cases only, ranging from robust samples with epidemiological data to case reports and case series from each country or regions. RESULTS: Overall, SCA3 is the most common form in the continent. Region-specific prevalence and ranking of the common forms vary. On the other hand, region-specific phenotypic variations were not consistently found based on the available literature analyzed, with the exception of the absence of epilepsy in SCA10 consistently described in a particular cluster of cases in South Brazil. CONCLUSION: Systematic, multinational studies analyzing in detail the true frequencies of SCAs across the Americas as well as distinct clinical signs and clues of each form would be ideal to look for these potential variations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".