A.03 Analyses of surgical and MRI factors associated with cerebellar mutism
Bibliographic record
Abstract
Background: The surgical risk factors and neuro-imaging characteristics associated with cerebellar mutism (CM) remain unclear and require further investigation. We aimed to examine surgical and MRI findings associated with CM in children following posterior fossa tumor resection. Methods: Using our data registry, we retrospectively collected data from pediatric patients who acquired CM and were matched based on age and pathology type with patients not acquiring CM after posterior fossa surgery. The strength of association between surgical and MRI variables and CM were examined using odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Results: A total of 22 patients were included. Medulloblastoma was the most common pathology among CM patients (91%). Tumor attachment to the floor of the fourth ventricle (OR, 6; 95% CI, 0.7-276), calcification/hemosiderin deposition (OR 7; 95% CI 0.9-315.5), and post-operative peri-ventricular ischemia on MRI (OR, 5; 95% CI, 0.5-236.5) were found to have the highest association with CM. Conclusions: Our results may suggest that tumor attachment to the floor of the fourth ventricle, pathological calcification, and post-operative ischemia are relatively more prevalent in patients with CM. Collectively, our work calls for a larger multi-institutional study of CM patients to further investigate the determinants and management of CM to potentially minimize its development and predict onset.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".