MBCL-34. STRUCTURAL CONNECTIVITY ABNORMALITY IN CHILDREN TREATED FOR MEDULLOBLASTOMA
Bibliographic record
Abstract
Curative treatments for medulloblastoma impart significant toxicity on the developing brain. Though treatment-related changes to the white matter connections have been described, there remains a limited understanding of treatment effect on the structural connectome: the dense, integrative network of white matter connections present in the brain. The connectome is thought to subserve complex and dynamic behaviors, therefore identifying areas of compromise within the connectome may help elucidate the mechanisms of toxicity common among medulloblastoma survivors. METHODS AND Magnetic resonance images were acquired for 12 medulloblastoma survivors and 13 age and gender-matched children using a Siemens 3-Tesla scanner at the Hospital for Sick Children in Toronto. Post-processing of MR images was completed using FreeSurfer and MRtrix3 software. Probabilistic streamlines were generated and used to estimate whole-brain structural connectivity matrices based on the length of white matter connections. Mass univariate testing of all white matter connections (FDR = 0.05) showed significantly shorter connections in survivors (p < 0.0001). Global connectivity metrics were also analyzed; we observed significantly longer path lengths in patients (p = 0.02) but no significant differences in clustering (p = 0.99) or small-worldness (p = 0.98). These results suggest perturbed inter-regional connectivity but spared local connectivity in medulloblastoma survivors. Treatment of medulloblastoma may disrupt inter-regional connectivity between distant brain regions. Future work will aim to identify the regions most affected by perturbed inter-regional connectivity, and to relate pertuberance along those connections to neuropsychological outcome in medulloblastoma survivors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".