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Record W4212821842 · doi:10.1177/10935266211018928

Ependymal Tumors

2022· article· en· W4212821842 on OpenAlexaff
Mariarita Santi, Angela N. Viaene, Cynthia Hawkins

Bibliographic record

VenuePediatric and Developmental Pathology · 2022
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsGrading (engineering)EpendymomaPathologyMedicineEpigeneticsSpinal cordPopulationBrain tumorBioinformaticsBiologyNeuroscienceGene

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.233
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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