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Record W2016852257 · doi:10.3747/co.v18i3.877

Managing Treatment-Related Adverse Events Associated with egfr Tyrosine Kinase Inhibitors in Advanced Non-Small-Cell Lung Cancer

2011· article· en· W2016852257 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCurrent Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineRashLung cancerEpidermal growth factor receptorErlotinibAdverse effectTyrosine kinaseEGFR inhibitorsCancerTyrosine-kinase inhibitorAfatinibOncologyDiarrheaInternal medicineReceptor

Abstract

fetched live from OpenAlex

Non-small-cell lung cancer (nsclc) has the highest prevalence of all types of lung cancer, which is the second most common cancer and the leading cause of cancer-related mortality in Canada. The need for more effective and less toxic treatment options for nsclc has led to the development of agents targeting the epidermal growth factor receptor (egfr)-mediated signalling pathway, such as egfr tyrosine kinase inhibitors (egfr-tkis). Although egfr-tkis are less toxic than traditional anti-neoplastic agents, they are commonly associated with acneiform-like rash and diarrhea. This review summarizes the clinical presentation and causes of egfr-tki-induced rash and diarrhea, and presents strategies for effective assessment, monitoring, and treatment of these adverse effects. Strategies to improve the management of egfr-tki-related adverse events should improve clinical outcomes, compliance, and quality of life in patients with advanced nsclc.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.040
GPT teacher head0.372
Teacher spread0.332 · 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