MétaCan
Menu
Back to cohort
Record W2057039211 · doi:10.3747/co.22.2430

Management of egfr tki—Induced Dermatologic Adverse Events

2015· review· en· W2057039211 on OpenAlexaffvenue
Barbara Melosky, Natasha B. Leighl, J. Rothenstein, Randeep Sangha, David J. Stewart, Kim Papp

Bibliographic record

VenueCurrent Oncology · 2015
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsProbity Medical ResearchLakeridge HealthBC Cancer AgencyPrincess Margaret Cancer CentreOttawa HospitalUniversity Health Network
FundersJohnson and JohnsonJanssen BiotechAstellas PharmaAstraZenecaCelgeneAmgenPfizerEli Lilly and Company
KeywordsMedicineParonychiaAdverse effectDermatologyGefitinibRashErlotinibEpidermal growth factor receptorEGFR inhibitorsLung cancerClinical trialChemotherapyCancerOncologyInternal medicine

Abstract

fetched live from OpenAlex

Targeting the epidermal growth factor receptor (egfr) pathway has become standard practice for the treatment of advanced non-small-cell lung cancer. Compared with chemotherapy, egfr tyrosine kinase inhibitors (tkis) have been associated with improved efficacy in patients with an EGFR mutation. Together with the increase in efficacy comes an adverse event (ae) profile different from that of chemotherapy. That profile includes three of the most commonly occurring dermatologic aes: acneiform rash, stomatitis, and paronychia. Currently, no randomized clinical trials have evaluated the treatments for the dermatologic aes that patients experience when taking egfr tkis. Based on the expert opinion of the authors, some basic strategies have been developed to manage those key dermatologic aes. Those strategies have the potential to improve patient quality of life and compliance and to prevent inappropriate dose reductions.

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 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 score0.976
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.265
GPT teacher head0.549
Teacher spread0.284 · 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.

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

Citations78
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueCurrent OncologySame topicLung Cancer Treatments and MutationsFrench-language works237,207