RANO seizure working group-Tumor-Related Epilepsy Assessment Tool (RANO-TREAT) to assess seizure control for glioma treatment trials and clinical practice
Bibliographic record
Abstract
BACKGROUND: No standardized method exists for seizure assessment in glioma clinical trials. We describe the development and evaluation of RANO-TREAT (Tumor Related Epilepsy Assessment Tool) for seizure assessment and its association with changes on brain MRI. METHODS: Patients with glioma/glioneuronal tumors and ≥ 1 prior seizure along with clinicians completed RANO-TREAT in conjunction with brain MRIs, yielding multiple RANO-TREAT scores at clinic visits over time. Unweighted (primary) and weighted (post-hoc) scores were correlated with disease progression via MRI in all patients and patients with IDHmt tumors, separately. Cohorts were randomly split by patient into cohort-specific training and validation sets. Weights for RANO-TREAT items were defined by multivariable generalized estimating equation models in cohort-specific training sets and validated in cohort-specific validation sets. A nomogram was developed using overall cohort training and validation sets. RESULTS: Four hundred and ninety patients (310 IDHmt tumors) had ≥ 1 visits and 285 patients (168 IDHmt tumors) had ≥ 2 visits. Unweighted RANO-TREAT scores (OR:1.01; 95%CI:0.998-1.02; P = .13) and score changes (OR:1.00; 95%CI:0.99-1.02; P = .63) were not associated with progressive disease on MRI. Post-hoc analysis using training and validation sets demonstrated weighted RANO-TREAT scores were correlated with progressive disease in both overall cohort validation set (OR:2.51; 95%CI:1.80-3.52; P < .0001) and IDHmt cohort validation set (OR:4.53; 95%CI:2.11-9.75; P = .0001). Weighted analyses for patients with ≥ 2 visits showed similar associations in validation sets. CONCLUSIONS: This prospective study suggests an association of seizure control evaluated by a new standardized tool with disease progression in glioma. This tool requires further systematic evaluation in glioma clinical trials alongside more traditional endpoints.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".