{"id":"W3012501245","doi":"10.2196/16467","title":"Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Samsung; National Research Foundation of Korea; National Research Foundation","keywords":"Visual inspection; Computer science; Artificial intelligence; Machine learning; Medical physics; mHealth; Cervical cancer; Medicine; Cancer; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005043057,0.000182317,0.0006331383,0.000201189,0.0001928594,0.00001108056,0.00006567616,0.00006483182,0.00006024911],"category_scores_gemma":[0.0001681957,0.0001633841,0.00005439022,0.000458685,0.000118541,0.00005356844,0.0000524967,0.0005063051,4.088257e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002001088,"about_ca_system_score_gemma":0.0006045027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002236174,"about_ca_topic_score_gemma":0.0004209752,"domain_scores_codex":[0.9979473,0.0002123777,0.0005897398,0.0003923487,0.0004288609,0.0004293778],"domain_scores_gemma":[0.9987209,0.0001974389,0.000274695,0.0001181286,0.0002281468,0.0004607094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1292097,0.004074322,0.6789118,0.06165585,0.0002638657,0.00002659277,0.02304654,0.0004059595,0.02417955,0.00006789681,0.0001937851,0.0779642],"study_design_scores_gemma":[0.02190106,0.1751731,0.5675508,0.001175986,0.0004975713,0.000008369504,0.01287214,0.1980477,0.02165296,0.0000139282,0.0006134826,0.0004928671],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915496,0.00112743,0.003784478,0.001374049,0.00002347133,0.001971787,0.0000392256,0.00008439208,0.00004553874],"genre_scores_gemma":[0.9947602,0.00006657838,0.003559071,0.001232044,0.0001456094,0.0001648696,0.00003016441,0.00003700509,0.000004476516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1976418,"threshold_uncertainty_score":0.6662611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1082264995306128,"score_gpt":0.4398185191740116,"score_spread":0.3315920196433988,"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."}}