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Record W2776970051 · doi:10.11641/pde.91.1_154

Gastric metastasis of renal cell carcinoma, presenting with melena

2017· article· en· W2776970051 on OpenAlexaff
Nanoka Chiya, Shin Nishii, Suguru Ito, Akinori Mizoguchi, H Terada, Hirotaka Furuhashi, Koji Maruta, Kazuhiko Shirakabe, Masaaki Higashiyama, Chikako Watanabe, Kengo Tomita, Ryota Hokari, Kimi Kato, Kuniaki Nakanishi, Shunsuke Komoto, Shigeaki Nagao

Bibliographic record

VenueProgress of Digestive Endoscopy · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsNational Defence Medical Centre
Fundersnot available
KeywordsMedicineMelenaRenal cell carcinomaMetastasisNephrectomyCancerInternal medicineGastroenterologyKidney

Abstract

fetched live from OpenAlex

A 70-year-old man with past medical history of left renal cell carcinoma status post nephrectomy in 1988 presented to our department, complaining of melena and anemia. He underwent partial gastrectomy for gastric metastasis in 2007, followed by pancreatectomy for pancreatic metastasis, by radiofrequency ablation for right renal metastasis (2009) , and by molecular target therapy for multiple liver metastasis and retroperitoneal metastasis (2012) . He also had an evidence of right adrenal metastasis in 2013. On gastrointestinal endoscopy, a protruding lesion covered with white moss was found on greater curvature of the upper part of gastric body (color 1, 2, 3) . The lesion showed bleeding diathesis during the procedure. The pathological findings of the lesion were consistent with the gastric metastasis of renal cell carcinoma. Although the patient’s initial symptoms, melena and anemia, were disappeared spontaneously after the biopsy, the patient decided to take palliative care including the deep sedation. Here, we report a case of gastric metastasis of renal cell carcinoma, which presented with gastrointestinal hemorrhage.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.425
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.305
Teacher spread0.280 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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