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Record W3132125306 · doi:10.1177/1066896921993229

Two Cases of Sporadic Eosinophilic Solid and Cystic Renal Cell Carcinoma in Manitoba Population

2021· article· en· W3132125306 on OpenAlexaffabout
Mohammad Mohaghegh, Shivani Mathur, Karl Kassier, Janetta Rossouw, Robert Wightman, Jeff Saranchuk, Ian W. Gibson

Bibliographic record

VenueInternational Journal of Surgical Pathology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsBrandon Regional Health AuthorityUniversity of Manitoba
Fundersnot available
KeywordsEosinophilicRenal cell carcinomaMedicineNeoplasmPathologyRenal massPopulationClear cellNephrectomyKidneyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Two sporadic cases of eosinophilic solid and cystic renal cell carcinoma (ESC RCC), at our institution, are presented in this study to contribute to the growing literature on this novel renal neoplasm. The first patient was a 38-year-old female with two synchronous renal masses measuring 3.5 and 1.9 cm on preoperative imaging. The second patient was a 44-year-old female with an incidental renal mass measuring 4 cm. Both patients underwent uncomplicated radical nephrectomies. The 1.9 cm mass in the first patient was consistent with clear cell RCC. The dominant mass in the first patient and the tumor in the second patient had microscopic and macroscopic findings in keeping with ESC RCC including a tan appearance, abundant eosinophilic cytoplasm, and CK20+ and CK7- staining. Both patients had an uncomplicated course following surgery with no evidence of local recurrence or distant metastatic disease for 1 and 2 years for the first and second patient accordingly. These cases contribute to a growing body of literature regarding ESC RCC including, to our knowledge, the first reported case of synchronous ESC RCC and clear cell RCC. Further research about this novel renal neoplasm is needed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.253

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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 designBench or experimental
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

Citations8
Published2021
Admission routes2
Has abstractyes

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