Multiple Administrations of Gadobutrol in the Pediatric Brain: No Change in T1 Signal at MRI
Bibliographic record
Abstract
Purpose To determine whether multiple doses of gadobutrol increase the T1 signal intensity in the brains of children. Materials and Methods This retrospective imaging study evaluated 91 children (median age, 5.4 years; age range, 0-17 years) with brain tumors who underwent five or more MR brain examinations at a single institution. A subgroup of 46 patients received five or more administrations of gadobutrol (0.1 mmol/kg) and underwent follow-up MRI. T1 signal intensity in the globus pallidus and dentate nucleus was measured at the first to sixth unenhanced MR brain examination in these children. Globus pallidus-to-corpus callosum and dentate nucleus-to-corpus callosum signal intensity ratios were analyzed by linear mixed-effect analysis. Subgroup analysis was performed for six children who underwent 14 or more administrations of gadobutrol. Results The globus pallidus-to-corpus callosum ratio increased with patient age (absolute change, 0.0052 per year; 95% confidence interval: 0.0033, 0.0071; P < .0001). There was no change in the dentate nucleus-to-corpus callosum ratio with age (P = .30). Among 46 children who received five or more doses of gadobutrol (median dose, 11 mL; range, 3.9-31 mL), there was no change in signal intensity ratio of the globus pallidus (P = .17) or dentate nucleus (P = .44). Among six children who underwent more than 14 administrations of gadobutrol (median dose, 64 mL; range, 40-91 mL) there was no change in signal intensity ratio of the globus pallidus (P = .15) or dentate nucleus (P = .50). Conclusion No increase in T1-weighted signal intensity ratio was observed in the globus pallidus or dentate nucleus after the administration of at least five doses of gadobutrol. © RSNA, 2018 Online supplemental material is available for this article.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".