Detecting Traumatic Brain Lesions in Children: CT versus MRI versus Susceptibility Weighted Imaging (SWI)
Bibliographic record
Abstract
Cranial CT scans are at the center of decision making in brain injuries in children because of their speed and ability to detect surgically relevant lesions. However, alternative techniques, such as conventional MRI may have advantages in terms of radiation exposure and sensitivity to detect brain injury. Susceptibility-weighted imaging (SWI), a relatively novel MRI sequence, shows promise in terms of its sensitivity in detecting hemorrhagic lesions; however, its clinical potential remains uncertain. In this observational study of children (5-16 years of age) with traumatic brain injury (TBI) at a tertiary pediatric emergency department (ED) we compared the ability of detecting traumatic brain lesions on acute CT and MRI/SWI ∼ 5 weeks post-injury based on detecting the presence or absence, extent, and type of traumatic brain lesions. We analyzed the results of 76 patients (53 male) after TBI (mean age 10.24 ± 2.50 years, range 5.75-14.67 years). Glasgow Coma Score was 13-15 in 54 patients (71%), 9-12 in 13 patients (17%) and <8 in 9 patients (12%). CTs were completed in the ED; MRI and SWI were completed at a mean of 36.11 ± 15.75 days post-injury. Detection of any lesions occurred on CT scan in 68%, on MRI in 54%, and on SWI in 86% of cases, and SWI detected additional lesions 30% of the time compared to CT and MRI. SWI may be more sensitive in detecting traumatic lesions than CT or MRI. This may be important for the ongoing management of TBIs and their prognosis.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".