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Record W2030524144 · doi:10.1097/spc.0b013e32832e4681

Management of sorafenib, sunitinib, and temsirolimus toxicity in metastatic renal cell carcinoma

2009· review· en· W2030524144 on OpenAlexaff
Catherine Guevremont, Ahmed Alasker, Pierre I. Karakiewicz

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2009
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill University Health CentreUniversité de Montréal
Fundersnot available
KeywordsSunitinibMedicineSorafenibTemsirolimusMucositisHypophosphatemiaPazopanibRenal cell carcinomaInternal medicineToxicityNeutropeniaSide effect (computer science)OncologyHepatocellular carcinomaPharmacologyGastroenterologyPI3K/AKT/mTOR pathwayDiscovery and development of mTOR inhibitors

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To review the common and serious toxicities associated with the use of tyrosine kinase inhibitors such as sorafenib and sunitinib and mTOR inhibitor temsirolimus, and to outline the most recent toxicity management guidelines. RECENT FINDINGS: Common grade 3 or 4 side effects with sorafenib include lymphopenia (13%), hypophosphatemia (13%), elevated lipase (12%), hand-foot syndrome (6%), and mucositis/stomatitis (6%). Common grade 3 or 4 side effects with suntinib elevated lipase (16%), neutropenia (12%), lymphopenia (12%), hypertension (8%), and fatigue/asthenia (7%). As for temsirolimus, common grade 3 or 4 side effects consist of anemia (20%), hyperglycemia (11%), fatigue/asthenia (11%), dyspnea (9%), and hypophosphatemia (5%). Intracranial hemorrhage (ICH) is rare but occurred in sorafenib-exposed and sunitinb-exposed patients. Cardiovascular morbidity may also be observed in sorafenib-exposed and sunitinib-exposed patients. SUMMARY: Through preventive and therapeutic measures, these side effects can be effectively managed, without reducing the dose and, therefore, affecting the efficacy of the treatment.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.161
GPT teacher head0.407
Teacher spread0.246 · 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 designOther design
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

Citations49
Published2009
Admission routes1
Has abstractyes

Explore more

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