Bibliographic record
Abstract
The comprehensive molecular profiling of cancer has resulted in new insights into the biology and classification of numerous tumor types. In the case of primary brain tumors that commonly affect adults, an emerging set of disease-defining biomarker sets is reshaping existing diagnostic entities that had previously been defined on the basis of their microscopic appearance. Substantial progress has been made in this regard for common primary brain tumors in adults, especially diffuse gliomas, where large-scale profiling efforts have led to the incorporation of highly prevalent molecular alterations that promote a biologically based classification as an adjunct to the traditional histopathologic approach. The growing awareness that histologically indistinguishable tumors can be divided into more precise and biologically relevant subgroups has demanded a more global routine approach to biomarker assessment. These considerations have begun to intersect with the decreasing costs and availability of genome-wide analysis tools and, thus, incorporation into routine practice. We review how molecular profiling already has led to an evolution in the classification of brain tumors. In addition, we discuss the likely trajectory of incorporation of global molecular profiling platforms into the routine clinical classification of adult brain tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".