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Record W2588308728 · doi:10.1159/000455970

Outcome of Patients with Renal Cell Carcinoma and Multiple Glandular Metastases Treated with Targeted Agents

2017· article· en· W2588308728 on OpenAlexaff
Paolo Grassi, Ludovic Doucet, Palma Giglione, Viktor Grünwald, Bohuslav Melichar, Luca Galli, Ugo De Giorgi, Roberto Sabbatini, Cinzia Ortega, Matteo Santoni, Aristotelis Bamias, Elena Verzoni, Lisa Derosa, Hana Študentová, Luca Porcu, Filippo de Braud, Camillo Porta, Giuseppe Procopio

Bibliographic record

VenueOncology · 2017
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineRenal cell carcinomaGastroenterologyInternal medicineCarcinomaUrology

Abstract

fetched live from OpenAlex

PURPOSE: Pancreatic metastases (PM) from renal cell carcinoma (RCC) have been associated with long-term survival. The aim of this study was to evaluate the outcome of RCC patients with multiple glandular metastases (MGM) treated with targeted therapies (TTs). METHODS: Sixty-four MGM patients treated between 1993 and 2014 were retrospectively identified from a database of 274 RCC patients with PM from 11 European centers. The survival of MGM patients was compared with that of both patients with PM only and a cohort of 325 RCC patients with non-GM (control group) treated with TTs. Survival was estimated using the Kaplan-Meier method and was statistically compared using the log-rank test. RESULTS: Fifty-six patients (88%) had at least 2 MGM, 7 patients (11%) had 3 MGM and 1 patient had 4 MGM, while non-GM were present in the remaining patients. The median overall survival (OS) was 54.2 months for MGM and 73.4 months for patients with PM only. The median OS in the control group was 22.7 months and statistically inferior to both MGM (p < 0.001) and PM patients (p < 0.001). CONCLUSION: MGM from RCC are associated with a remarkable survival. Despite some limitations, these findings suggest that GM might be considered a predictor of a favorable prognosis.

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 score0.046
Threshold uncertainty score0.500

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 studies0.0000.000
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 head0.032
GPT teacher head0.280
Teacher spread0.248 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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