{"id":"W4405374842","doi":"10.1016/j.iotech.2024.100889","title":"5MO Novel radiologic phenotypes of chaotic tumor angiogenesis associated with poor ICI outcomes in NSCLC","year":2024,"lang":"en","type":"article","venue":"Immuno-Oncology Technology","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Syöpäsäätiö; Helsingin Yliopisto; Jane ja Aatos Erkon Säätiö; Ida Montinin Säätiö","keywords":"Angiogenesis; Phenotype; Chaotic; Medicine; Oncology; Internal medicine; Cancer research; Computer science; Biology; Genetics; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003446333,0.0003687989,0.001315052,0.001281118,0.00004565826,0.00000715046,0.0002914051,0.0006184911,0.00009912848],"category_scores_gemma":[0.0004168433,0.0002596845,0.0001959966,0.00133015,0.000691306,0.0000518478,0.0001326242,0.0004514211,0.00005007793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009314618,"about_ca_system_score_gemma":0.0004749702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363087,"about_ca_topic_score_gemma":0.0001712031,"domain_scores_codex":[0.9979125,0.00008839267,0.0006351176,0.0005691919,0.0001961396,0.0005987184],"domain_scores_gemma":[0.9985861,0.0004806094,0.0002036885,0.000544135,0.0001280964,0.00005743513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001193176,0.002710015,0.5336233,0.0002139427,0.003163102,0.005489861,0.0004465832,0.00003610703,0.4301256,0.004278411,0.0002652745,0.01845463],"study_design_scores_gemma":[0.01602091,0.01845795,0.7769913,0.001111322,0.002165845,0.004520182,0.001536937,0.0005291282,0.1721695,0.001703279,0.003832289,0.000961397],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820441,0.01034754,0.00009173504,0.004468973,0.000297847,0.0007948118,0.00002550827,0.0004770564,0.00145245],"genre_scores_gemma":[0.9971887,0.00008918233,0.001652027,0.0002453672,0.00002391178,0.0002106977,0.00003885971,0.00005833579,0.0004929058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2579561,"threshold_uncertainty_score":0.9999855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01844260851793054,"score_gpt":0.274020598656686,"score_spread":0.2555779901387555,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}