<scp>CNS</scp> embryonal tumours: <scp>WHO</scp> 2016 and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Embryonal tumours of the central nervous system (CNS) present a significant clinical challenge. Many of these neoplasms affect young children, have a very high mortality and therapeutic strategies are often aggressive with poor long-term outcomes. There is a great need to accurately diagnose embryonal tumours, predict their outcome and adapt therapy to the individual patient's risk. For the first time in 2016, the WHO classification took into account molecular characteristics for the diagnosis of CNS tumours. This integration of histological features with genetic information has significantly changed the diagnostic work-up and reporting of tumours of the CNS. However, this remains challenging in embryonal tumours due to their previously unaccounted tumour heterogeneity. We describe the recent revisions made to the 4th edition of the WHO classification of CNS tumours and review the main changes, while highlighting some of the more common diagnostic testing strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it