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Record W4321596316 · doi:10.1002/pbc.30267

Technical advances in the surgical management of Wilms tumors in children

2023· article· en· W4321596316 on OpenAlexaff
Rodrigo Romao, Alida F. W. van der Steeg, Marcus M. Malek, Sabine Irtan, Kenneth W. Gow, Khalil Ghandour, David Biasoni, Andrew M. Davidoff, Max Pachl

Bibliographic record

VenuePediatric Blood & Cancer · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineWilms' tumorGeneral surgeryPediatric SurgeonInvasive surgeryPediatric surgeryUltrasonographyMedical physicsSurgeryIntensive care medicineRadiologyPathology

Abstract

fetched live from OpenAlex

Surgery is one of the cornerstones of Wilms tumor treatment. In this article, we present technical advancements that are finding their way into the armamentarium of pediatric cancer surgeons. We discuss the current approaches, challenges, opportunities, and future directions of minimally invasive surgery (laparoscopic and robotics), image-guided surgery, and fluorescence-guided surgery. Furthermore, we discuss the use of intraoperative ultrasonography, as well as the use of new techniques to improve the quality of lymph node sampling.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.249
Teacher spread0.245 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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