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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 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)0.030
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.007
Open science0.0010.001
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.002

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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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