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Record W4246419861 · doi:10.1038/sj.labinvest.3700732

Gynecologic

2008· article· en· W4246419861 on OpenAlexaff
Zisong Zhou, E Sotelo, Pheroze Tamboli, Bogdan Czerniak, Bamidele Adeagbo, Timothy Goldsmith, Sharad Ghamande, Keni Gu, V Anandan, Christopher A. Moosavi, H Arabi, Rouba Ali‐Fehmi, Subhayu Bandyopadhyay, L Fathalla, Duangpen Thirabanjasak, Adnan Munkarah, Robert Morris, Silva Wayne, Margaret Assaad, C Otis, Sharon Marconi, D Aximu, Terence J. Colgan, Amee D. Azad, Ruoyu Ni, Nanji Ss

Bibliographic record

VenueLaboratory Investigation · 2008
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

to selected 48 cases of renal carcinomas of various subtypes, 7 oncocytomas and 13 normal kidney. Histological examination focused on the presence of small branching glands, stromal cellularity, ovarian-type stroma, prominent vessels and estrogen and progesterone receptors. Results: This study included 26 CN and 13 MEST. Patient' age (53.2 vs 53.8 years) and tumor size (6.5 vs 6.8 cm) were similar between CN and MEST. CN predominantly affected women (male/female=2/24), while MEST exclusively affected female. CN and MEST had many similar histological features, including size of cysts, stromal cellularity, presence of ovarian-type stroma, calcification and hemorrhage. ER and PR were positive in 4/5 (80%) and 3/5 (60%) CN, and 5/8 (62.5%) and 5/7 (71.4%) MEST. However, prominent vessels (4/13, 30.8%) and small branching glands (53.8%) were exclusively seen in MEST (p=0.000). Clustering analysis demonstrated that CN and MEST had very similar molecular profiles (Figure Of differentially expressed genes that best distinguish CN/MEST from other subtypes of kidney tumors, the highest and lowest differentially expressed genes are insulin-like growth factor 2 and carbonic anhydrase II, respectively.

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 score0.007
Threshold uncertainty score0.435

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 head0.040
GPT teacher head0.245
Teacher spread0.205 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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