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Record W2324184778 · doi:10.1097/spc.0b013e3283644c30

Side-effects associated with targeted therapies in renal cell carcinoma

2013· review· en· W2324184778 on OpenAlexaff
Denis Soulières

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2013
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineRenal cell carcinomaToxicityDiseaseIntensive care medicineKidney cancerTargeted therapyBioinformaticsCancerOncologyPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: With the introduction of multiple new targeted agents for the treatment of metastatic renal cell carcinoma, specific attention must be given to toxicities induced by these agents. RECENT FINDINGS: Agents with activity on the same target can have different toxicities, which might lead the clinician to select or individualize therapies. However, some data also support the concept of maximal tolerated dose as a marker of efficacy. Specific elements of these data are summarized and discussed in the paper. SUMMARY: Toxicity management is pivotal in individualized cancer care. This papers outlines some of the principles related to disease and toxicity management.

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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.105
GPT teacher head0.370
Teacher spread0.265 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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