Treatment response of CNS high‐grade neuroepithelial tumors with <i>MN1</i> alteration
Bibliographic record
Abstract
Central nervous system high-grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1) is a rare recently described entity. Fourteen CNS HGNET-MN1 patients were identified using genome-wide methylation arrays/RT-PCR across seven institutions. All patients had surgery (gross total resection: 10; subtotal resection: four) as initial management followed by observation alone in three patients, followed by radiotherapy in eight patients (focal: five; craniospinal: two; CyberKnife: one) and systemic chemotherapy in three patients. Seven patients relapsed; five local and two metastatic, despite adjuvant radiotherapy, of which three died. Treatment of CNS HGNET-MN1 remains a major treatment challenge despite aggressive surgical resections and upfront radiotherapy, warranting new approaches to this rare malignancy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".