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Record W4399470268 · doi:10.1016/j.ejcped.2024.100169

European standard clinical practice recommendations for primary pediatric low-grade gliomas

2024· article· en· W4399470268 on OpenAlexaff
Kleoniki Roka, Katrin Scheinemann, Shivaram Avula, John H. Maduro, Ulrich W. Thomale, Astrid Sehested, A.Y.N. Schouten-Van Meeteren

Bibliographic record

VenueEJC Paediatric Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcMaster Children's Hospital
Fundersnot available
KeywordsMedicinePrimary (astronomy)Clinical PracticePediatricsFamily medicine

Abstract

fetched live from OpenAlex

Pediatric low-grade gliomas are the most common brain tumours in childhood and adolescence. Despite the excellent prognosis, pediatric low-grade glioma survivors may suffer from variable long-term complications and may require repeated therapies, implying that this is a chronic disease. The current review describes the European Standard Clinical Practice recommendations for low-grade gliomas at primary diagnosis, that were developed on behalf of SIOPe BTG LGG Working Group within the framework of European Reference Network PaedCan. The manuscript describes the diverse spectrum of pediatric low-grade gliomas in terms of location, age, underlying cancer predisposition syndromes, and special circumstances, such as infantile chiasmatic hypothalamic glioma and diencephalic syndrome, as well as current diagnostic criteria and indications for treatment. Furthermore, it provides current knowledge in histopathology and molecular pathology. Finally, the review focuses on the need for a multidisciplinary approach and treatment indications providing a guide on current treatment modalities, used as first-line therapy in Europe along with information on adverse effects, and follow-up.

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)0.018
metaresearch head score (Gemma)0.061
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: Methods · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0050.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.009

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 head0.046
GPT teacher head0.410
Teacher spread0.363 · 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
GenreMethods

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

Citations8
Published2024
Admission routes1
Has abstractyes

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