{"id":"W4323045941","doi":"10.21203/rs.3.rs-2526701/v1","title":"Reproducible and Clinically Translatable Deep Neural Networks for Cancer Screening","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Overfitting; Triage; Software portability; Artificial intelligence; Machine learning; Computer science; Deep learning; Cervical cancer; Receiver operating characteristic; Medicine; Population; Visual inspection; Cancer; Artificial neural network; Medical emergency; Internal medicine","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":[0.002215525,0.001179912,0.0005016996,0.0004553747,0.0002591219,0.000780453,0.001671429,0.001074213,0.001476153],"category_scores_gemma":[0.008590332,0.0003747676,0.0005299743,0.0004678685,0.000541622,0.0008316261,0.001260632,0.001658947,0.0007229511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236595,"about_ca_system_score_gemma":0.001829079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008822531,"about_ca_topic_score_gemma":0.01187734,"domain_scores_codex":[0.9989968,0.0003636916,0.00005076191,0.0002654111,0.0002234879,0.00009984965],"domain_scores_gemma":[0.9985424,0.0006131244,0.0001564331,0.0002629394,0.000356308,0.0000688209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003615083,0.0003758205,0.01129931,0.0002509601,0.0002071577,0.0002096716,0.0000863538,0.6702743,0.01133567,0.002687688,0.01278414,0.2901274],"study_design_scores_gemma":[0.00002679693,0.0001052116,0.001058205,0.00003590752,0.00002259077,0.00003640932,0.00002087749,0.9893439,0.004531492,0.003028284,0.001778217,0.0000122646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2685861,0.004967717,0.701467,0.003934382,0.0005810611,0.0004407346,0.003235647,0.009342805,0.007444601],"genre_scores_gemma":[0.8436639,0.0008475762,0.1461658,0.001006038,0.000109912,0.0003707497,0.003884942,0.0002903134,0.003660634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008822531,"threshold_uncertainty_score":0.01754236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3035939631796585,"score_gpt":0.5295118604188808,"score_spread":0.2259178972392223,"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."}}