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Record W4405374842 · doi:10.1016/j.iotech.2024.100889

5MO Novel radiologic phenotypes of chaotic tumor angiogenesis associated with poor ICI outcomes in NSCLC

2024· article· en· W4405374842 on OpenAlexaff
Vamsidhar Velcheti, Rhea Chitalia, Carla Leibowitz, Kai Zhang, Anant Madabhushi, Nathaniel Braman

Bibliographic record

VenueImmuno-Oncology Technology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersSyöpäsäätiöHelsingin YliopistoJane ja Aatos Erkon SäätiöIda Montinin Säätiö
KeywordsAngiogenesisPhenotypeChaoticMedicineOncologyInternal medicineCancer researchComputer scienceBiologyGeneticsGeneArtificial intelligence

Abstract

fetched live from OpenAlex

VEGF and immune checkpoint inhibitor (ICI) combination treatments have recently shown efficacy in non-small cell lung cancer (NSCLC), highlighting the role of tumor vasculature in outcomes. Conventional biomarkers focus on genetic or immune factors, but overlook the vascular complexity of tumors. We discovered two Imaging Tumor Angiogenesis Phenotypes (ITAP) in patients with advanced NSCLC associated with overall survival (OS) and progression-free survival (PFS).

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 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 score0.258
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
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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
Published2024
Admission routes1
Has abstractyes

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