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 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 | 0.144 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".