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Record W4405783144 · doi:10.3390/curroncol32010004

Vulvar Metastasis in Renal Cell Carcinoma: A Case Report Highlighting the Aggressive Nature of Clear Cell Renal Cell Carcinoma

2024· article· en· W4405783144 on OpenAlexvenueno aff
Andreea Boiangiu, Ana-Maria Cioca, Gabriel-Petre Gorecki, Romina‐Marina Sima, Liana Pleș, Marius-Bogdan Novac, Ionuţ Simion Coman, Valentin Titus Grigorean, Vasile Lungu, Mihai Géorgescu, G Filipescu

Bibliographic record

VenueCurrent Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVulvaMetastasisRenal cell carcinomaVulvar cancerClear cell carcinomaCarcinomaCervixCancerDermatologyLichen sclerosusLabiaSex organPathologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Vulvar cancer is one of the rarest gynecological malignancies. The development of this condition can be associated with either dysplasia linked to human papillomavirus (HPV), primarily affecting younger women, or vulvar dermatoses such as lichen sclerosus, which predominantly affect older women. Over the last decade, the incidence of vulvar cancer has risen by 0.6% annually, while the relative survival rate has declined. Although metastasis to the vulva is uncommon, it can occur, particularly from cancers in nearby organs such as the cervix, bladder, rectum, or anus. More rarely, metastases from breast cancer and renal cell carcinoma have been reported in the vulva. Vaginal metastases from clear cell renal carcinoma are especially rare. In this article, we present the case of a 56-year-old patient diagnosed with clear cell renal carcinoma, who came to our clinic with a lesion on the right labia, which was identified as a metastasis originating from the kidney. Given the rarity of genital metastases in renal cancer, such cases should be examined and discussed to encourage further research and studies.

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)0.003
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.346
Teacher spread0.298 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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