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Record W3127294621 · doi:10.2217/cer-2020-0173

Factors affecting treatment selection and overall survival for first-line EGFR-tyrosine kinase inhibitor therapy in non-small-cell lung cancer

2021· article· en· W3127294621 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineOsimertinibLung cancerOncologyInternal medicineRetrospective cohort studyTyrosine-kinase inhibitorTyrosine kinaseCohortCancerTargeted therapyErlotinibEpidermal growth factor receptor

Abstract

fetched live from OpenAlex

Aim: To investigate the factors associated with treatment selection and overall survival for first-line EGFR-tyrosine kinase inhibitors (EGFR-TKIs) therapy among patients with non-small-cell lung cancer. Materials & methods: We conducted a retrospective cohort study of linked administrative health databases in Ontario, Canada. Results: A total of 1011 patients received an EGFR-TKI as first-line therapy. Treatment selection and overall survival associated with these treatments were affected by age, sex, geographical residency, comorbidities and different sites of metastasis. Conclusion: Though recent approval of osimertinib offers a potential new standard of care in the first-line setting, earlier generation TKIs remain pillars in treatment of non-small-cell lung cancer therapeutic armamentarium. Our findings may contribute to optimizing treatment sequencing of EGFR-TKIs to maximize clinical benefits.

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)0.001
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 score0.264
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.118
GPT teacher head0.468
Teacher spread0.351 · 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