The clinical significance of equivocal findings on spinal MRI in children with medulloblastoma
Bibliographic record
Abstract
BACKGROUND: Medulloblastoma (MB) is the most common malignant brain tumor of childhood, with cerebrospinal fluid spread the most common site of metastasis. Currently, children diagnosed with MB and evidence of spinal metastasis are treated with an increased dose of craniospinal radiation (CSI). This report reviewed equivocal abnormalities including nerve root clumping, linear vascular enhancement, nerve root enhancement and/or other vague findings on spinal magnetic resonance imaging (MRI) to elucidate their prognostic significance and aid in risk stratification. METHODS: This retrospective cohort study identified children (≥3 years) diagnosed with MB between 1988 and 2012. Children treated with upfront CSI were included, and staging spine MRI must have been done preoperatively or within 72 hr of primary tumor resection. Initial MRI of the spine was assessed by two independent reviewers blinded to outcome to evaluate for equivocal findings. Survival analysis was done to determine impact on prognosis. RESULTS: One hundred of 157 patients were eligible for the analysis. Equivocal findings were identified in 48 (48%) patients, with MRI done preoperatively in 45 (94%) patients. Analysis by subgroup identified a higher proportion of equivocal findings in the sonic hedgehog (SHH) subgroup (P = 0.007). Five-year overall survival (OS) in children with equivocal findings compared to those with normal MRI was not different, 80 vs. 84.8% respectively, while OS in M3 patients was worse at 54.7% (P = 0.02). CONCLUSION: A higher proportion of equivocal findings was identified in the SHH subgroup. This institutional retrospective review demonstrates equivocal findings are common, not associated with decreased OS and should not prompt increased dose of CSI.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".