Invited Article: The expanding impact of molecular biology on the diagnosis and treatment of gliomas
Bibliographic record
Abstract
For nearly a century, glial neoplasms have been classified by microscopic features alone with treatment prescribed based on histology using a "one-size-fits-all" formula. However, recent advances in our understanding of the molecular events underlying gliomagenesis are beginning to change the way we think about the diagnostic classification of gliomas. Indeed, several recurring molecular derangements are now being viewed as cornerstones of a new diagnostic framework because these alterations appear to be superior to traditional microscopic classification schemes as guideposts for treatment selection and prognosis. Moreover, molecular analysis of tumor tissue is identifying aberrant growth signaling pathways in glioma which can now be blocked selectively by a new generation of targeted therapies, including small molecule inhibitors and monoclonal antibodies. Time will tell whether these new agents can be successfully introduced into the clinical arena. In the meantime, the molecular characteristics of gliomas are being used to select patients for both randomized trials and phase II studies.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".