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Record W4402737361 · doi:10.14740/wjon1899

An Extremely Rare Case of Primary Malignant Melanoma of the Kidney

2024· article· en· W4402737361 on OpenAlexvenueno aff
Akane Onogi, Daichi Kodama, Naoki Watanabe, Takashi Ishida, Hisao Komeda, Shuji Mikami, Takuji Tanaka

Bibliographic record

VenueWorld Journal of Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMelanomaPrimary (astronomy)DermatologyCancer research

Abstract

fetched live from OpenAlex

Malignant melanoma (MM) is a tumor that usually occurs in the skin, but this malignant tumor can also develop in extracutaneous tissues, including urogenital tissues. In regard to MM occurring in urogenital tissues, bladder origin is common but renal primary MM is extremely rare. In the Department of Emergency and Urology at Gifu Municipal Hospital, a tumor of the right kidney was detected in a computed tomography scan to determine the cause of severe pain in the lower extremities of a 45-year-old Japanese woman. With the clinical diagnosis of renal cell carcinoma, resection of the right kidney was performed under laparoscopy. The cut surface of the tumor encapsulated by a thick fibrous capsule was dark brown, and the tumor cells with large nuclei, large nucleoli, acidophil cytoplasm, and numerous melanin granules showed papillary, solid, or alveolar growth. Immunohistochemically, the tumor cells were positive for Melan A and human melanoma black 45 (HMG45) but negative for transcription factor E3 (TFE3), transcription factor EB (TFEB), cytokeratin 7 (CK7), carbonic anhydrase 9 (CA9), and AEl/AE3. We conducted careful and detailed examinations, including an association of the patient's medical history, but there were no indications for tumors, particularly MM, in any organs. Therefore, she was ultimately diagnosed with primary kidney MM.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0040.002
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 head0.011
GPT teacher head0.272
Teacher spread0.262 · 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 designCase report
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
Published2024
Admission routes1
Has abstractyes

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