Diagnostic Algorithm for Episodic Ataxia Types 1 and 2 in Resource-Limited Settings

A supplementary document proposing a clinical diagnostic algorithm for distinguishing episodic ataxia types 1 and 2. The algorithm is based on a systematized literature review and statistical analysis of clinical features, and was tested on a recent case cohort. The document was authored by Claudio M. de Gusmao and last updated in April 2026.

Use Cases

• Developing a clinical decision support tool based on attack duration and symptom specificity.
• Training a classifier to differentiate EA1 from EA2 based on features like myokymia and nystagmus.
• Validating diagnostic models using the reported sensitivity (87.5%) and specificity (94.0%) metrics.

Strengths

• Algorithm is based on statistical analysis of clinical features with reported high specificity (e.g., 99.6% for myokymia in EA1).
• Proposed method was tested on a recent case cohort, yielding a sensitivity of 87.5% for identifying EA2.
• Document is openly licensed under CC-BY-4.0.

Limitations

• The dataset is a 37.1 KB DOCX file, indicating a very limited textual scope rather than a structured data table.
• Row count and column-level documentation are absent; the content must be manually extracted and interpreted.
• Data may reflect publication bias inherent to the literature review methodology.

Provenance

Source

figshare

Collection Method

Systematized literature review and statistical analysis of published cases with confirmed pathogenic variants.

Time Range

Literature review timeframe is unspecified; algorithm was tested on cases from the last three years prior to publication.

Freshness

Last updated 2026-04-21 14:38:15

Geography

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