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Adolescents and Young Adults With a “Rare” Cancer: Getting Past Semantics to Optimal Care for Patients With Germ Cell Tumors

2014· editorial· en· W2107480310 on OpenAlexaff
Sara Stoneham, Juliet Hale, Carlos Rodríguez‐Galindo, Ha Dang, Thomas A. Olson, Matthew J. Murray, James F. Amatruda, Claire Thornton, G. Suren Arul, Deborah F. Billmire, Mark Krailo, Dan Stark, Al Covens, Jean Hurteau, Sally Stenning, James C. Nicholson, David M. Gershenson, A. Lindsay Frazier

Bibliographic record

VenueThe Oncologist · 2014
Typeeditorial
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Cancer InstituteTeenage Cancer Trust
KeywordsGerm cell tumorsYoung adultGerm cellMedicineSemantics (computer science)CancerOncologyInternal medicineBiologyChemotherapyComputer science

Abstract

fetched live from OpenAlex

Because the tumors of adolescence and young adulthood (AYA) are distinct from those that occur earlier and later in life, the most common tumors in this age group are termed “rare.” We offer a collaborative, cross-disciplinary, evidence-based approach, advocated and funded by civil society, to advance the field of germ cell tumor and potentially to apply to other rare AYA tumors.

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)0.007
metaresearch head score (Gemma)0.034
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.015
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0030.002
Research integrity0.0150.032
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.004
GPT teacher head0.252
Teacher spread0.247 · 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
GenreEditorial

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

Citations18
Published2014
Admission routes1
Has abstractyes

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