rain Neoplasms and Coagulation—Lessons from Heterogeneity
Bibliographic record
Abstract
The coagulation system constitutes an important facet of the unique vascular microenvironment in which primary and metastatic brain tumors evolve and progress. While brain tumor cells express tissue factor (TF) and other effectors of the coagulation system (coagulome), their propensity to induce local and peripheral thrombosis is highly diverse, most dramatic in the case of glioblastoma multiforme (GBM), and less obvious in pediatric tumors. While the immediate medical needs often frame the discussion on current clinical challenges, the coagulation pathway may contribute to brain tumor progression through subtle, context-dependent, and non-coagulant effects, such as induction of inflammation, angiogenesis, or by responding to iatrogenic insults (e.g. surgery). In this regard, the emerging molecular diversity of brain tumor suptypes (e.g. in glioma and medulloblastoma) highlights the link between oncogenic pathways and the tumor repertoire of coagulation system regulators (coagulome). This relationship may influence the mechanisms of spontaneous and therapeutically provoked tumor cell interactions with the coagulation system as a whole. Indeed, oncogenes (EGFR, MET) and tumor suppressors (PTEN, TP53) may alter the expression, activity, and vesicular release of tissue factor (TF), and cause other changes. Conversely, the coagulant microenvironment may also influence the molecular evolution of brain tumor cells through selective and instructive cues. We suggest that effective targeting of the coagulation system in brain tumors should be explored through molecular stratification, stage-specific analysis, and more personalized approaches including thromboprophylaxis and adjuvant treatment aimed at improvement of patient survival.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".