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Record W2948757664 · doi:10.21873/anticanres.13402

Long-term Prognostic Impact of Chromosome Abnormalities in Clear Cell Renal Cell Carcinoma

2019· article· en· W2948757664 on OpenAlexaff
Carlotta Palumbo, Maria Furlan, Piera Balzarini, Tiziano Zanotelli, A. Cozzoli, Alessandro Veccia, Simone Francavilla, Stefania Zamboni, Regina Tardanico, Claudio Simeone, Alessandro Antonelli

Bibliographic record

VenueAnticancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsClear cell renal cell carcinomaRenal cell carcinomaProportional hazards modelChromosome abnormalityOncologyClear cellInternal medicineChromosomeCytogeneticsKidney cancerPathologyBiologyMedicineChromosome 7 (human)CancerKaryotypeGenetics

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: Clear cell renal cell carcinoma (ccRCC) shows variable chromosomal abnormalities. The aim of this study was to assess the prognostic role of ccRCC chromosomal abnormalities in a single-center cohort with an extended follow-up. MATERIALS AND METHODS: A systematic cytogenetic analysis was performed in 283 consecutive surgically-treated patients for renal masses between 1997 and 2002. Kaplan-Meier and multivariable Cox regression (MCR) models were used to calculate cancer specific survival (CSS). RESULTS: Among 174 ccRCC patients, the most common abnormality was deletion in chromosome 3 (54.6%). At a median follow-up of 119 months, 38 patients (21.8%) died from RCC. At MCR models, worse CSS was independently predicted by deletions in chromosomes 2, 19, 20 or 22 and insertions in chromosome 18. CONCLUSION: Specific ccRCC chromosomal abnormalities are independently associated with worse CSS. Cytogenetic evaluation may direct further genetic analysis for personalized prognostic stratification.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.371
Teacher spread0.316 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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