Bibliographic record
Abstract
At the recent Society for Neuro-Oncology meeting in Montreal, more than a few clinicians and scientists concluded (as they do every year) that little progress has been made in the treatment of malignant primary brain tumors. Skepticism persists among neuro-oncologists for good reason. Despite the discovery of novel therapeutic targets, innovative methods of drug delivery, and sophisticated imaging modalities, patient survival has scarcely changed in 30 years. Sadly, we have accumulated much information, but little useful knowledge. In addition to a lack of progress in treating primary brain tumors, the threat of nervous system toxicity as a consequence of chemotherapy and cranial irradiation looms ever larger, not just in patients with nervous system cancer, but in all patients with cancer. In fact, a good case can be made that the nervous system has supplanted the bone marrow as the dose-limiting end organ for much of modern-day cancer therapy. Clearly we need a new paradigm for developing drugs in the laboratory and selecting drugs in the clinic. In this issue of Neurology ®, Gong et al.1 address these …
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".