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Record W4389734685 · doi:10.33590/emj/10301071

Redefining Renal Cell Carcinoma: A Molecular Perspective on Classification and Clinical Implications

2023· article· en· W4389734685 on OpenAlexaff
Arjun Athreya Raghavan, Ian W. Gibson, Robert Wightman, Piotr Czaykowski, Jeffrey Graham

Bibliographic record

VenueEuropean Medical Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsRenal cell carcinomaTFE3Kidney cancerClear cellMedicinePathologyCancer researchBiologyComputational biologyGeneGenetics

Abstract

fetched live from OpenAlex

Renal cell carcinoma (RCC) is the most common primary tumour of the kidney. RCC is a clinically and pathologically heterogenous entity, which has traditionally been classified under two broad categories: clear-cell and non-clear cell. With improved molecular diagnostic methodologies and genetic testing, the classification of RCC has shifted from a morphological basis to a molecular/genetic focus, and has been systematically updated to reflect these advancements. The new 2022 World Health Organization (WHO) classification of RCC is the most recent of these updates, and contains significant changes, as compared to the previous 2016 classification. The most substantial of these changes is the establishment of a new category of molecularly-defined RCC, including TFE3-rearranged RCC, TFEB-altered RCC, ELOC-mutated RCC, fumarate hydratase-deficient RCC, succinate dehydrogenase-deficient RCC, ALK-rearranged RCC, and SMARCB1-deficient renal medullary carcinoma. In this narrative review, the authors briefly summarise the histopathological characteristics, clinical course, current treatment standards, and future treatment directions of each of these molecularly-defined RCC subtypes.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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 head0.067
GPT teacher head0.350
Teacher spread0.282 · 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 designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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