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Record W2900717648 · doi:10.1111/his.13791

Reactivity of <scp>CK</scp>7 across the spectrum of renal cell carcinomas with clear cells

2018· article· en· W2900717648 on OpenAlexaff
Manuel Lora Gonzalez, Reza Alaghehbandan, Kristýna Pivovarčíková, Květoslava Michalová, Joanna Rogala, Petr Martínek, Maria Pané, Enric Condom, Éva Compérat, Monika Ulamec, Milan Hora, Michal Michal, Ondřej Hes

Bibliographic record

VenueHistopathology · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of British ColumbiaRoyal Columbian Hospital
FundersUniverzita Karlova v Praze
KeywordsClear cellCytokeratinPathologyRenal cell carcinomaCellClear cell renal cell carcinomaMedicinePapillary renal cell carcinomasNeoplasmImmunostainingOncocytomaStainingImmunohistochemistryBiology

Abstract

fetched live from OpenAlex

AIMS: Current available data on cytokeratin 7 (CK7) immunostaining pattern in the clear cell renal cell carcinoma (RCC) spectrum is conflicting. The aim of this study was to assess CK7 immunoreactivity within the spectrum of clear cell renal neoplasms, including clear cell RCC, multicystic renal neoplasm of low malignant potential and clear cell papillary RCC-like tumours. METHODS AND RESULTS: We analysed two clones of CK7 and two tumour blocks for a total of 75 cases divided into five distinct groups: (i) low-grade clear cell RCC, (ii) high-grade clear cell RCC, (iii) multicystic renal neoplasm of low malignant potential, (iv) clear cell RCC with cystic changes and (v) clear cell papillary RCC-like tumours. We found the highest CK7 reactivity in low-grade clear cell RCC, multicystic renal neoplasm of low malignant potential and clear cell papillary RCC-like groups, ranging from 60% to 93%. CONCLUSIONS: Our findings show that CK7 immunoreactivity in clear cell RCC is variable, and the extent of staining depends on the grade and architectural growth patterns of the tumours.

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 score0.311
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.242
Teacher spread0.226 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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