The Brain in Children: Is Contrast Enhancement Really Needed after Obtaining Normal Unenhanced CT Results?
Bibliographic record
Abstract
PURPOSE: To retrospectively determine and compare the sensitivity and specificity of unenhanced and contrast material-enhanced computed tomography (CT) (reference standard) in the diagnosis of brain abnormalities and to evaluate any change in diagnosis that resulted from the contrast-enhanced study. MATERIALS AND METHODS: This study was approved by the local research ethics board; the requirement for informed consent was waived. The authors reviewed the unenhanced and contrast-enhanced CT scans of the brain obtained in 353 children for indications other than trauma. There were 196 boys and 157 girls aged 0 months to 17.8 years. Scans were read independently by two pediatric neuroradiologists who were blinded to clinical information. The diagnosis for each scan was recorded according to the anatomic section (supratentorial, infratentorial, ventricles, and skull). The final diagnosis was classified as normal, abnormal, or equivocal. kappa Statistics, with 95% confidence intervals, were reported, and the sensitivity, specificity, positive predictive value, and negative predictive value were calculated. RESULTS: Interreader agreement for different anatomic regions varied between good (kappa coefficient, 0.63) and very good (kappa coefficient, 0.88) for unenhanced and contrast-enhanced scans. Sensitivity, specificity, positive predictive value, and negative predictive value for unenhanced scans were 97%, 89%, 87%, and 97%, respectively. The use of contrast material led to a change in the original normal or equivocal diagnosis to an abnormal diagnosis for only five (2.7%) of the 183 normal unenhanced scans. CONCLUSION: Unenhanced CT of developing brains has high sensitivity and specificity in the diagnosis of pathologic findings. The use of intravenous contrast material after unenhanced CT of the brain in children did not change the diagnosis frequently.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".