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Record W2166103708 · doi:10.1177/1756287214528023

Innovations in the management of Wilms’ tumor

2014· review· en· W2166103708 on OpenAlexaff
Joseph M. Gleason, Armando J. Lorenzo, Paul R. Bowlin, Martin A. Koyle

Bibliographic record

VenueTherapeutic Advances in Urology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineWilms' tumorIntensive care medicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Advances in the management of Wilms' tumor have been dramatic over the past half century, not in small part due to the institution of multimodal therapy and the formation of collaborative study groups. While different opinions exist in the management of Wilms' tumors depending on where one lives and practices, survival rates have surpassed 90% across the board in Western societies. With more children surviving into adulthood, the concerns about morbidity have reached the forefront and now represent as much a consideration as oncologic outcomes these days. Innovations in treatment are on the horizon in the form of potential tumor markers, molecular biological means of testing for chemotherapeutic responsiveness, and advances in the delivery of chemotherapy for recurrent or recalcitrant tumors. Other technological innovations are being applied to childhood renal tumors, such as minimally invasive and nephron-sparing approaches. Risk stratification also allows for children to forego potentially unnecessary treatments and their associated morbidities. Wilms' tumor stands as a great example of the gains that can be made through protocol-driven therapy with strenuous outcomes analyses. These gains continue to spark interest in minimization of morbidity, while avoiding any compromise in oncologic efficacy. While excitement and innovation are important in the advancement of treatment delivery, we must continue to temper this enthusiasm and carefully evaluate options in order to continue to provide the highest standard of care in the management of this now highly curable disease.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.998
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.335
Teacher spread0.318 · 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 teacher head, 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

Citations43
Published2014
Admission routes1
Has abstractyes

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