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

A proposed clinical management algorithm for distinguishing episodic ataxia types 1 and 2, developed by Claudio M. de Gusmao and published on figshare in April 2026. The algorithm is based on a systematized literature review and statistical analysis of clinical features, such as attack duration and triggers, and was tested on a recent case cohort.

Use Cases

• Supporting clinical decision-making for episodic ataxia based on described features like attack duration and triggers.
• Developing rule-based diagnostic tools using the proposed algorithm's logic and reported sensitivity/specificity metrics.
• Training medical students or practitioners on differential diagnosis of episodic ataxia using the documented clinical distinctions.

Strengths

• Algorithm tested on a recent case cohort with a reported sensitivity of 87.5% for identifying EA2.
• Specific diagnostic cut-offs are provided, such as attack duration <10 minutes having 75.3% sensitivity and 94.0% specificity for EA1.
• Published under a CC-BY-4.0 license, allowing for open reuse and adaptation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 34.7 KB file size suggests the dataset is a document outlining the algorithm, not a large-scale clinical data repository.

Provenance

Source

figshare

Collection Method

Systematized literature review and statistical analysis of individuals with confirmed pathogenic variants in KCNA1 and CACNA1A genes.

Time Range

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

Freshness

Last updated 2026-04-21 14:38:15; freshness should be verified.

Geography

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