CLRM-10 METIS (EF-25): A PIVOTAL, RANDOMIZED CONTROLLED STUDY OF TUMOR TREATING FIELDS IN PATIENTS WITH 1–10 BRAIN METASTASES FROM NON-SMALL CELL LUNG CANCER
Bibliographic record
Abstract
Abstract BACKGROUND Tumor Treating Fields (TTFields) are electric fields that disrupt cancer cell division. TTFields treatment showed efficacy in preclinical non-small cell lung cancer (NSCLC) models. Furthermore, TTFields therapy improved survival with a tolerable safety profile in patients with glioblastoma. The objective of the pivotal METIS trial [NCT02831959] is to evaluate the efficacy and safety of TTFields therapy in NSCLC patients with brain metastases. METHODS NSCLC patients (N=270) with 1–10 brain metastases will be randomized 1:1 to stereotactic radiosurgery (SRS) followed by continuous TTFields therapy using NovoTTF-200M (150 kHz, recommended >18 h/day) with best standard of care (BSC) or SRS followed by BSC alone. Follow-ups will be conducted every 2 months until second intracranial progression. Key inclusion criteria are: Karnofsky Performance Status ≥70, new diagnosis of 1 inoperable or 2–10 supra- and/or infratentorial brain metastases from NSCLC amenable to SRS, and optimal therapy for extracranial disease. Key exclusion criteria are: prior whole brain radiotherapy, single operable, or recurrent brain metastases. Primary endpoint is time to first intracranial progression. Secondary endpoints include time to neurocognitive failure, overall survival, radiological response rate (RANO-BM and RECIST V1.1), quality of life, adverse events, time to first/second intracranial progression for patients with 1–4 and 5–10 brain metastases, bi-monthly intracranial progression rate from 2–12 months, and time to second intracranial and distant progression. The study is powered at 80% (2-sided alpha of 0.05) to detect a hazard ratio of 0.57. In July 2021, an independent Data Monitoring Committee (DMC) reviewed the study data and recommended continuation as planned. The trial is currently recruiting at 92 sites in North America, Europe, Israel, mainland China and Hong Kong.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".