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Record W2124694649 · doi:10.1196/annals.1414.005

Advances in the Management of Pediatric Central Nervous System Tumors

2008· review· en· W2124694649 on OpenAlexaff
Shaker Abdullah, Ibrahim Qaddoumi, Éric Bouffet

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2008
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsSubspecialtyMedicineNeurosurgeryIntensive care medicineRadiation oncologyModalitiesRadiation therapyMultidisciplinary approachTreatment modalityRadiosurgeryPathologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Central nervous system tumors are the most common pediatric solid tumors and a leading cause of cancer-related mortality and morbidity in this age group. Survival rates have improved significantly over the last decades for most of the tumor types, as a consequence of improvements in neuroimaging, neurosurgery and neuroanesthesia, radiation oncology, and medical oncology. The complexity of the management of these patients requires a multidisciplinary approach and has led to the emergence of a new subspecialty of pediatric neuro-oncologists who are dedicated to the management and follow-up of this population. This review highlights the most critical advances in the diagnostic and treatment modalities of pediatric brain tumors. A specific review of the most common tumor types discusses treatment options, controversies, and ongoing developments, with an emphasis on cooperative trials.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.113
GPT teacher head0.378
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2008
Admission routes1
Has abstractyes

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