Imaging Methods for New-Onset Seizures at Canadian Pediatric Hospitals
Bibliographic record
Abstract
BACKGROUND: Seizures are common neurological events in children, with neuroimaging playing a crucial role in evaluating new-onset seizures. While magnetic resonance imaging (MRI) is often preferred over computed tomography (CT) for pediatric seizure imaging due to higher sensitivity and lack of ionizing radiation, practices regarding imaging protocols and sedation use vary. Currently, there are no published Canada-wide guidelines describing the practices for workup of pediatric seizures. METHODS: A cross-sectional survey was conducted among radiologists at 16 Canadian tertiary pediatric centers to assess neuroimaging practices for children with new-onset seizures. The survey explored the presence and content of dedicated seizure MRI protocols, sedation use, and strategies to facilitate non-sedated MRI. RESULTS: Fifteen centers (94%) responded. Only 2 (13%) reported using dedicated new-onset seizure protocols, while 10 (67%) used epilepsy-specific MRI protocols, and the others used different approaches, including variations of routine brain MRI. MRI sequences varied across institutions. Sedation use also varied, with a median sedation age range of 3 months to 6 years. Non-sedated MRI techniques such as feed-and-swaddle (93%) and natural sleep (27%) were commonly used for infants. Video goggles (67%) and child life specialist support (53%) were often used for older children. Only 2 institutions (13%) had fast MRI protocols, and virtual reality preparation was uncommon (13%). CONCLUSION: Considerable variability exists in MRI protocols and sedation practices across Canadian pediatric centers evaluating new-onset seizures. Our findings emphasize the need for national consensus guidelines to standardize imaging protocols, reduce sedation use, and optimize care for pediatric seizure patients.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".