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Record W3092827384 · doi:10.14740/wjon1325

Evolving Paradigms in the Management and Outcomes of Sarcomatoid Renal Cell Carcinoma in the Era of Immune Checkpoint Inhibitors

2020· review· en· W3092827384 on OpenAlexvenueno aff
Ragia Aly, Amandeep Aujla, Sachin Gupta, Ruby Gupta, Sorab Gupta, Sheila Kalathil

Bibliographic record

VenueWorld Journal of Oncology · 2020
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSunitinibRenal cell carcinomaSarcomatoid carcinomaImmune checkpointInternal medicineOncologyNivolumabCancerCarcinomaImmunotherapy

Abstract

fetched live from OpenAlex

Renal cell carcinoma (RCC) is a common cancer that affects a significant number of patients every year around the world. The presence of sarcomatoid features in these tumors is considered a poor prognostic feature. Patients with RCC with sarcomatoid features had significantly worse outcomes when treated with sunitinib, the previous first-line standard of care therapy when compared to patients without such features. Multiple immune checkpoint inhibitors have recently been approved for the treatment of RCC. In this article, we review the literature available on the outcomes of patients with sarcomatoid RCC treated with immune checkpoint inhibitors.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.314
Teacher spread0.280 · 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
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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