Meta-topologies define distinct anatomical classes of brain tumors linked to histology and survival
Bibliographic record
Abstract
Fresh tissue was acquired from the resected tumour and the surrounding white matter during craniotomy.The specimens, having been sliced with a vibratome, underwent standard monotonic and repetitive deformation with atomic force microscope (AFM) nanoindentation, in order to determine the elastic modulus.The influence of isocitrate dehydrogenase gene family (IDH) mutation status, World Health Organization (WHO) grade, age and preconditioning on tumour and adjacent white matter elasticity was investigated with linear mixed-effects models.Results: On standard monotonic deformation, tissues from IDH-wildtype cases were found softer than tissues from IDH-mutant ones in grade III patients (p¼0.049),but similar in elasticity to IDH-mutant cases in grade II patients (p¼0.48).The glioma was softer, although non-significantly, than the peritumoral white matter in grade III patients (p¼0.07) and similar in elasticity to the adjacent brain in grade II (p¼0.49) and IV patients (p¼0.59).On repetitive indentation, both the adjacent white matter (p¼0.003) and tumour tissue (p¼0.002)initially manifested stiffening and eventually softening.The elasticity of the peritumoral white matter was restored to its initial values (p¼0.94).In the glioma tissue, stiffening was only partially reversed (p¼0.015).Conclusion: Diffuse glioma tissue and peritumoral white matter elasticity represents a phenotypic trait linked to established histopathological characteristics.Sources of heterogeneity on local measurements between patient groups and within individual patients should be considered and explored by further research.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".