{"id":"W3165316018","doi":"10.1111/apm.13156","title":"Reliability of Ki67 visual scoring app compared to eyeball estimate and digital image analysis and its prognostic significance in hormone receptor‐positive breast cancer","year":2021,"lang":"en","type":"article","venue":"Apmis","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Breast cancer; Reliability (semiconductor); Hormone receptor; Digital image analysis; Computer science; Receptor; Oncology; Digital image; Internal medicine; Artificial intelligence; Computer vision; Cancer; Image (mathematics); Medicine; Image processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004929497,0.000320976,0.0003672158,0.001370269,0.0002187047,0.0004850851,0.0003165419,0.0003483485,0.0005172478],"category_scores_gemma":[0.01072452,0.0001937024,0.0003232589,0.0007267222,0.0004142632,0.0003495254,0.000535138,0.0001873401,0.0002526192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000181031,"about_ca_system_score_gemma":0.00014858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000974107,"about_ca_topic_score_gemma":0.001249442,"domain_scores_codex":[0.9964893,0.001590837,0.0003297444,0.0006240889,0.0008485455,0.000117439],"domain_scores_gemma":[0.9915132,0.003360567,0.00172551,0.001034883,0.002165744,0.0002000498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001476425,0.00008494109,0.9470336,0.0001529739,0.0002580306,0.0001359443,0.0007999368,0.0004690996,0.01172705,0.000063716,0.0001837612,0.0376144],"study_design_scores_gemma":[0.00002657933,0.0008375471,0.9865632,0.00002915627,0.0001816924,0.0009389117,0.000332835,0.00277941,0.00733028,0.0001418155,0.0008091332,0.0000293871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994687,0.001247987,0.002593638,0.00002107971,0.00003346438,0.00003400398,0.0001114983,0.00004129038,0.001229962],"genre_scores_gemma":[0.9983826,0.000136958,0.001059193,0.00001097267,0.00001739571,0.0000182859,0.00009081233,0.0000105183,0.0002732224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004929497,"threshold_uncertainty_score":0.02607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005917145370934098,"score_gpt":0.2731437133189146,"score_spread":0.2672265679479805,"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."}}