Bibliographic record
Abstract
Ependymomas (EPN) are commonly encountered brain tumors in the pediatric population. They may arise in the supratentorial compartment, posterior fossa and spinal cord. Histopathologic grading of EPN has always been challenging with poor interobserver reproducibility and lack of correlation between histologic grade and patient outcomes. Recent studies have highlighted that, despite histopathological similarities among variants of EPN at different anatomical sites, they possess site-specific genetic and epigenetic alterations, transcriptional profiles and DNA copy number variations. This has led to a molecular and location-based classification for EPN which has been adopted by the World Health Organization Classification of Central Nervous System Tumors and more accurately risk-stratifies patients than histopathologic grading alone. Given the complexity of this evolving field, the purpose of this paper is to offer a practical approach to the diagnosis of EPN, including the selection of the most appropriate molecular surrogate immunohistochemical stains, basic molecular studies and more sophisticated techniques if needed. The goal is to reach a rapid, sound diagnosis, providing essential information regarding prognosis and guiding clinical decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".