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Record W1859889471 · doi:10.5489/cuaj.2846

Renal vein leiomyosarcoma and renal cell carcinoma presenting together: A case report and discussion on the follow up.

2015· article· en· W1859889471 on OpenAlexvenueno aff
Conor M. Devlin, Kanwar R. Gill, Jennifer A. Thomas, Chandra Shekhar Biyani

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal cell carcinomaRenal veinLeiomyosarcomaConfusionRadiological weaponRadiologyPerioperativeSurgeryGeneral surgeryKidneyInternal medicine

Abstract

fetched live from OpenAlex

Leiomyosarcoma affecting the renal vein is rare, with about 30 documented cases in the English literature. The appearance on computed tomography can be difficult to interpret and is often confused with advanced renal cell carcinoma (RCC). This confusion can have implications on the perioperative care of patients presenting with this disease. We report a case with an usual radiological appearance of a renal vein leiomyosarcoma, alongside a separate RCC. This case highlights the need for a high index of suspicion in radiological reporting and provides a dilemma in regards to postoperative surveillance.

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)0.004
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0040.002
Scholarly communication0.0020.004
Open science0.0020.003
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.242
Teacher spread0.209 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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