MétaCan
Menu
Back to cohort
Record W4411709014 · doi:10.21037/tlcr-2024-1034

Response of non-small cell lung cancer harboring different epidermal growth factor receptor mutations to ablative radiotherapy

2025· article· en· W4411709014 on OpenAlexaffabout
Areej Al Rabea, Ian J. Gerard, Paul Daniel, Sophie Camilleri‐Broët, Ayman Oweida, Siham Sabri, Bassam Abdulkarim

Bibliographic record

VenueTranslational Lung Cancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversité de SherbrookeMcGill University Health CentreMcGill University
Fundersnot available
KeywordsAblative caseEpidermal growth factor receptorMedicineLung cancerCancer researchRadiation therapyLungOncologyReceptorBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

Background: and provide a deeper understanding of the mechanisms of response and resistance to SABR. Methods: assessment included colony formation, cell viability, and proliferation assays. Tumor formation was assessed by subcutaneous injection of pre-irradiated cells in yellow fluorescent protein (YFP)/severe combined immunodeficiency (SCID) mice. All mice were sourced from the Animal Resource Division at the McGill University Healthcare Centre. Response to SABR was evaluated in mice injected subcutaneously with isogenic cells and followed with sham or 34 Gy treatment. Tumors collected from both groups were evaluated for SABR effect histologically. Results: . Conclusions: mutations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.433
Teacher spread0.394 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueTranslational Lung Cancer ResearchSame topicLung Cancer Treatments and MutationsFrench-language works237,207