Neuroimaging to diagnose central nervous system tumours in children
Bibliographic record
Abstract
QUESTION: Headache, vomiting, lethargy, and seizures are common symptoms in healthy children with benign viral illnesses, but they are also signs that could represent a central nervous system (CNS) tumour. Primary care providers and guardians are hesitant to expose children to radiation associated with computed tomography scans or take on risks associated with the sedation frequently needed for magnetic resonance imaging. When should primary care providers order radiologic head imaging for children with common symptoms to identify those with a CNS tumour? ANSWER: Central nervous system tumours have no pathognomonic features, which often results in delays in diagnosis. Owing to the high prevalence of infratentorial tumours, children commonly present with symptoms of increased intracranial pressure, making a detailed history and a comprehensive physical examination, including ophthalmoscopy for papilledema, especially important. Magnetic resonance imaging is the criterion standard test but it may take time to access, and young children may need sedation. Hence, computed tomography may be a preferable first option.The HeadSmart initiative in the United Kingdom provides guidance to obtain brain imaging within 4 weeks of onset of persistent symptoms that are associated with CNS tumours. We advocate applying the same criteria in Canada in order to reduce delay in diagnosis of CNS tumours in children.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".