Neuroimaging Findings and Repeat Neuroimaging Value in Pediatric Chronic Ataxia
Bibliographic record
Abstract
BACKGROUND: Chronic ataxia, greater than two months in duration, is encountered relatively commonly in clinical pediatric neurology practise and presents with diagnostic challenges. It is caused by multiple and diverse disorders. Our aims were to describe the neuroimaging features and the value of repeat neuroimaging in pediatric chronic ataxia to ascertain their contribution to the diagnosis and management. MATERIALS AND METHODS: A retrospective charts and neuroimaging reports review was undertaken in 177 children with chronic ataxia. Neuroimaging in 130 of 177 patients was also reviewed. RESULTS: Nineteen patients had head computed tomography only, 103 brain magnetic resonance imaging only, and 55 had both. Abnormalities in the cerebellum or other brain regions were associated with ataxia. Neuroimaging was helpful in 73 patients with 30 disorders: It was diagnostic in 9 disorders, narrowed down the diagnostic possibilities in 14 disorders, and revealed important but non-diagnostic abnormalities, e.g. cerebellar atrophy in 7 disorders. Having a normal magnetic resonance imaging scan was mostly seen in genetic diseases or in the early course of ataxia telangiectasia. Repeat neuroimaging, performed in 108 patients, was generally helpful in monitoring disease evolution and in making a diagnosis. Neuroimaging was not directly helpful in 36 patients with 10 disorders or by definition the 55 patients with unknown disease etiology. CONCLUSIONS: Normal or abnormal neuroimaging findings and repeat neuroimaging are very valuable in the diagnosis and management of disorders associated with pediatric chronic ataxia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".