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Record W4399140656 · doi:10.1002/pbc.31118

Current surgical approaches to pediatric renal tumors

2024· review· en· W4399140656 on OpenAlexaff
Richard D. Glick, Rodrigo Romao, Max Pachl, Meera Kotagal, Amanda F Buchanan, Andrew J. Murphy, Elisabeth T. Tracy, Luca Pio, Nicholas G. Cost, Jan Godziński, Peter F. Ehrlich

Bibliographic record

VenuePediatric Blood & Cancer · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsMedicineMultidisciplinary approachPediatric oncologyIntensive care medicineCancerPediatric SurgeonPediatric cancerKidney cancerPediatric surgeryChildhood cancerSurgical resectionMultidisciplinary teamGeneral surgeryInternal medicineSurgeryNursing

Abstract

fetched live from OpenAlex

Pediatric renal tumors are among the most common pediatric solid malignancies. Surgical resection is a key component in the multidisciplinary therapy for children with kidney tumors. Therefore, it is imperative that surgeons caring for children with renal tumors fully understand the current standards of care in order to provide appropriate surgical expertise within this multimodal framework. Fortunately, the last 60 years of international, multidisciplinary pediatric cancer cooperative group studies have enabled high rates of cure for these patients. This review will highlight the international surgical approaches to pediatric patients with kidney cancer to help surgeons understand the key differences and similarities between the European (International Society of Pediatric Oncology) and North American (Children's Oncology Group) recommendations.

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 score0.003
Threshold uncertainty score0.011

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.069
GPT teacher head0.319
Teacher spread0.251 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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