{"id":"W4389666230","doi":"10.21203/rs.3.rs-3639521/v1","title":"Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Cancer Research","funders":"Congressionally Directed Medical Research Programs; National Institutes of Health; Ministerie van Volksgezondheid, Welzijn en Sport; Cancer Research UK","keywords":"Ductal carcinoma; Breast cancer; Medicine; Oncology; Pathology; Carcinoma in situ; Internal medicine; Cancer; Carcinoma","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":[0.0004184342,0.0003100626,0.0004259654,0.002108413,0.0001824614,0.0008469958,0.0003351619,0.0003783444,0.001742476],"category_scores_gemma":[0.001653142,0.0001207696,0.0004468375,0.001314671,0.0002134312,0.0003044275,0.0003999934,0.0002931282,0.0004448229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003056938,"about_ca_system_score_gemma":0.0003503282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001297223,"about_ca_topic_score_gemma":0.00111716,"domain_scores_codex":[0.9997876,0.00005240897,0.00001747591,0.00004623408,0.00006851892,0.00002771476],"domain_scores_gemma":[0.9995182,0.000135479,0.000128673,0.00004844401,0.0001265324,0.00004274589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001840943,0.0005523691,0.4151185,0.0002497701,0.0004145035,0.0004076843,0.000207848,0.05903376,0.1054613,0.00388937,0.003556586,0.4092673],"study_design_scores_gemma":[0.00004603422,0.00050024,0.3502619,0.00003127851,0.0002115303,0.001084863,0.0002314771,0.6220654,0.01840097,0.004964703,0.002139085,0.00006261949],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9423105,0.000317513,0.05175271,0.0002605012,0.00006124734,0.00006379688,0.0009879095,0.0004865681,0.003759329],"genre_scores_gemma":[0.9820477,0.0001005501,0.01596673,0.00003148028,0.00002739977,0.00002662494,0.000806256,0.00002775484,0.0009655902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002108413,"threshold_uncertainty_score":0.005829155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05106935378893597,"score_gpt":0.4021340426060759,"score_spread":0.35106468881714,"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."}}