{"id":"W4408795937","doi":"10.1016/j.labinv.2024.103822","title":"1580 Neighborhood Clustering Analysis to Define Epithelial-Stromal Interface for Tumor Infiltrating Lymphocyte Evaluation","year":2025,"lang":"en","type":"article","venue":"Laboratory Investigation","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Stromal cell; Cluster analysis; Lymphocyte; Interface (matter); Computer science; Pathology; Computational biology; Immunology; Medicine; Biology; 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":[],"consensus_categories":[],"category_scores_codex":[0.001141791,0.0001994172,0.000250607,0.0005526712,0.0002504466,0.0002728159,0.0004541869,0.00007924461,0.000008697261],"category_scores_gemma":[0.000606512,0.000225606,0.00007883969,0.003828643,0.00003906868,0.0008117894,0.000141504,0.0001027743,0.00001575656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007791601,"about_ca_system_score_gemma":0.0007185735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006171153,"about_ca_topic_score_gemma":0.0002459712,"domain_scores_codex":[0.9980557,0.0002063126,0.0004817117,0.0005961518,0.0003868884,0.0002732195],"domain_scores_gemma":[0.9981697,0.0001509137,0.0002489228,0.0005801998,0.0007326599,0.0001176716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001278427,0.00006019335,0.01920147,0.0002296789,0.0007864282,0.000001841579,0.004120362,0.3918094,0.4658655,0.01962268,0.0009355021,0.09723914],"study_design_scores_gemma":[0.0005919458,0.0001180139,0.00830978,0.00007006607,0.0002327564,6.869966e-7,0.00008002466,0.8008091,0.1852744,0.003683042,0.0006089151,0.0002212712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2649586,0.0001099393,0.7323058,0.0009244246,0.0006844011,0.0006055296,0.00002390803,0.0001964321,0.0001909601],"genre_scores_gemma":[0.9077815,0.000001768688,0.09017776,0.001465949,0.0001186614,0.0003933911,0.00001941223,0.00001334858,0.00002825059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6428229,"threshold_uncertainty_score":0.9199947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623950670233624,"score_gpt":0.2937445918472119,"score_spread":0.2775050851448757,"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."}}