{"id":"W4387827378","doi":"10.1016/j.eswa.2023.122209","title":"Measurement of adverse cosmesis in breast cancer: A deep learning approach","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Juravinski Hospital; Juravinski Cancer Centre","funders":"Hamilton Health Sciences Foundation","keywords":"Cosmesis; Artificial intelligence; Computer science; Support vector machine; Preprocessor; Receiver operating characteristic; Breast cancer; Medicine; Machine learning; Medical physics; Cancer; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001238346,0.0007937322,0.0005942428,0.001007926,0.0001371369,0.0005179383,0.0007264059,0.0006293479,0.0004793717],"category_scores_gemma":[0.002230435,0.0002249013,0.0005695267,0.0006429961,0.0002359587,0.0003899349,0.000606549,0.0007552239,0.0001211707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007040315,"about_ca_system_score_gemma":0.0004780356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002657886,"about_ca_topic_score_gemma":0.003189646,"domain_scores_codex":[0.9995968,0.0001169503,0.00004114533,0.0001031984,0.00009409757,0.00004784276],"domain_scores_gemma":[0.999223,0.000365546,0.0001458677,0.00005058688,0.0001707548,0.00004415718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009138107,0.0007313651,0.06106506,0.0004645777,0.000489116,0.000209835,0.0001039129,0.3230952,0.01380366,0.0008210038,0.003994943,0.5943075],"study_design_scores_gemma":[0.00004281794,0.0004704189,0.01836502,0.00005626166,0.0001203209,0.0001682683,0.00004289363,0.9726856,0.005034075,0.00198806,0.001002653,0.00002349194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.601543,0.006708712,0.3838966,0.0009838549,0.0001267609,0.0003173937,0.001830818,0.001134521,0.003458359],"genre_scores_gemma":[0.9389623,0.0009143357,0.05741161,0.000222956,0.00005987796,0.0002250564,0.001195049,0.00002819055,0.0009805064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002657886,"threshold_uncertainty_score":0.00654906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160392917268913,"score_gpt":0.2544504878006874,"score_spread":0.2328465586279983,"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."}}