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Record W4401029882 · doi:10.1016/j.lungcan.2024.107898

Treatment patterns and outcomes in KRAS‐positive advanced NSCLC patients previously treated with immune checkpoint inhibitors: A Canada-wide real-world, multi-center, retrospective cohort study

2024· article· en· W4401029882 on OpenAlexafffundabout
Samir H. Barghout, Luna Jia Zhan, Starvroula Raptis, F. Al-Agha, Niki Esfahanian, Aimee Popovacki, Goulnar Kasymjanova, Francis Proulx-Rocray, Sze Wah Samuel Chan, Matthew Richardson, M. Catherine Brown, Devalben Patel, Michelle L. Dean, Vishal Navani, Erica Moore, Lane Carvery, Elizabeth Yan, Daniel Goldshtein, Jasmine Cleary-Gosine, A. Gibson, Lynn Hubley, Karmugi Balaratnam, Tran B. Ngo, Azee Gill, Morgan Black, Adrian G. Sacher, Penelope A. Bradbury, Frances A. Shepherd, Natasha B. Leighl, Parneet Cheema, Sara Kuruvilla, Jason Agulnik, Shantanu Banerji, Rosalyn A. Juergens, Normand Blais, Winson Y. Cheung, Paul Wheatley‐Price, Geoffrey Liu, Stephanie Snow

Bibliographic record

VenueLung Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsWilliam Osler Health SystemLondon Health Sciences CentreBrampton Civic HospitalCancerCare ManitobaOttawa HospitalUniversity Health NetworkOccupational Cancer Research CentreJuravinski Cancer CentreUniversity of OttawaJewish General HospitalQueen Elizabeth II Health Sciences CentrePrincess Margaret Cancer CentreUniversity of CalgaryCentre Hospitalier de l’Université de MontréalHamilton Health Sciences
FundersCanadian Institutes of Health ResearchAmgen CanadaAmgen
KeywordsMedicineOncologyRetrospective cohort studyInternal medicineCohortKRASCenter (category theory)Cancer

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.281
Teacher spread0.275 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations8
Published2024
Admission routes3
Has abstractno

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