Recurrent Ataxia in Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: Recurrent ataxia is encountered infrequently in clinical pediatric neurology practise and presents with diagnostic challenges. It is caused by several disorders. Our aims were to describe the epidemiology and clinical features in children with recurrent ataxia. MATERIALS AND METHODS: A retrospective review was undertaken in 185 children with chronic ataxia, who presented during 1991 to 2008. Several databases were searched to ensure optimum ascertainment. Patients with brain tumors or isolated disorders of the peripheral nerves or vestibular system were excluded. RESULTS: Recurrent ataxia was reported in 21 patients. Their age range was between 6 and 32.75 years (males=12). The crude period prevalence rate for the 18-year study period was 7.44/100,000. Eight patients had episodic ataxia and seven had inflammatory and metabolic disorders. In the rest the etiology was unknown. Many patients presented with ataxia, dizziness, and vertigo. The frequency and duration of the ataxic episodes varied from several per day to one every few months. Other clinical features included developmental delay and seizures. Neuroimaging in episodic ataxia was normal and abnormal in inflammatory or metabolic disorders. Acetazolamide provided symptomatic relief in patients with episodic ataxia, while steroids were beneficial in patients with an inflammatory etiology. One child with a metabolic disorder died. CONCLUSIONS: Recurrent ataxia is an uncommon presentation in children and mortality is rare. Genetic, metabolic, and inflammatory disorders should be considered in these patients. Neuroimaging is essential. Acetazolamide in selected patients provides good symptomatic relief.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".