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Record W4246615405 · doi:10.4081/oncol.2010.1

Sequencing or not sequencing multikinase inhibitors in kidney cancer: this is the dilemma

2011· article· en· W4246615405 on OpenAlexaff
Chiara Paglino, Camillo Porta

Bibliographic record

VenueOncology Reviews · 2011
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineTemsirolimusSunitinibPazopanibSorafenibEverolimusBevacizumabKidney cancerOncologyRenal cell carcinomaInternal medicineCancerClinical trialPharmacologyDiscovery and development of mTOR inhibitorsHepatocellular carcinomaPI3K/AKT/mTOR pathwayChemotherapy

Abstract

fetched live from OpenAlex

With the recent development of targeted therapies (Sorafenib, Sunitinib, Temsirolimus, Bevacizumab plus Interferon-a, Everolimus and now also Pazopanib) patients with advanced renal cell carcinoma (RCC) now have a wide range of treatment options, all of which have shown both relevant clinical activity and manageable safety profile. This abundance of active treatments, coupled with relatively limited information, we have gathered from registrative phase III trials have raised the question of how to use these agents optimally...

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.193
GPT teacher head0.361
Teacher spread0.169 · 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.

Study designNot applicable
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
Published2011
Admission routes1
Has abstractyes

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