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Renal Cell Carcinoma Subtypes and Associated Renal Malignancies: A Pictorial Review—Part II

2022· article· en· W4210755410 on OpenAlexaff
Naveenjyote S. Boora, Michaela Perlau, Christopher Fung

Bibliographic record

VenueContemporary Diagnostic Radiology · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsHealth Sciences CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineRenal cell carcinomaAngiomyolipomaMalignancyOncocytomaRenal oncocytomaKidneyPathologyBiopsyInternal medicine

Abstract

fetched live from OpenAlex

In this second article in this series, we present a pictorial review of common renal neoplasms and renal malignancy mimics, including urothelial carcinoma, liposarcoma, oncocytoma, renal pseudotumors, and renal metastases. Renal cell carcinoma (RCC) subtypes and their respective radiologic findings were discussed in Part I of this series. Principles of management of select RCC subtypes and other renal neoplasms, including urothelial carcinoma and angiomyolipoma (AML), will be discussed. Management of solitary renal masses via active surveillance (AS) or surgical intervention and the indications for renal mass biopsy as part of the diagnostic workup will also be reviewed.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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