{"id":"W3210154426","doi":"10.1016/j.compbiomed.2021.104985","title":"Machine learning-based statistical analysis for early stage detection of cervical cancer","year":2021,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"AI in cancer detection","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University","keywords":"Random forest; Artificial intelligence; Computer science; Feature selection; Machine learning; Cervical cancer; Transformation (genetics); Pattern recognition (psychology); Decision tree; Tree (set theory); Feature (linguistics); Cancer; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002782386,0.00008479947,0.0002955886,0.0001640846,0.00005111605,0.000005211069,0.0001271681,0.00008794125,0.00002001998],"category_scores_gemma":[0.00009045619,0.000072048,0.00003373031,0.0005806318,0.0001637436,0.00003564086,0.00006027186,0.0001519203,1.819514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004490373,"about_ca_system_score_gemma":0.00004940754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005100215,"about_ca_topic_score_gemma":0.0006767437,"domain_scores_codex":[0.999097,0.0001415467,0.0002251066,0.0003212055,0.00006757663,0.0001475806],"domain_scores_gemma":[0.9991221,0.0004972402,0.00008569124,0.0001546059,0.0000876717,0.00005273157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002935177,0.0001129753,0.6605971,0.000223673,0.0004494516,0.00002991853,0.0008293387,0.01187305,0.007628541,0.01673491,0.00004372036,0.3011838],"study_design_scores_gemma":[0.001311933,0.0007187338,0.190897,0.00003285424,0.00007339224,0.000002998657,0.00001246801,0.8008443,0.002591018,0.001853439,0.001565119,0.0000967302],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05241785,0.0008354316,0.9452629,0.0008684809,0.0004821051,0.00007974744,0.00001056219,0.00002690079,0.00001599346],"genre_scores_gemma":[0.9779279,0.0001125135,0.02154009,0.0002862696,0.00007098293,0.00001933946,0.00001949506,0.00000340472,0.00001996903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9255101,"threshold_uncertainty_score":0.2938032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01943209428028205,"score_gpt":0.3247012478427908,"score_spread":0.3052691535625088,"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."}}