{"id":"W4284891101","doi":"10.3390/jpm12071114","title":"Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Endometriosis Research and Treatment","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Louise and Alan Edwards Foundation; Medical Research Council; Israel Science Foundation","keywords":"Endometriosis; Medicine; Population; Retrospective cohort study; Biobank; Logistic regression; Gynecology; Machine learning; Artificial intelligence; Internal medicine; Computer science; Bioinformatics; Environmental health; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004537826,0.0002066547,0.0008336794,0.0007641666,0.0007048393,0.00001629516,0.0002406257,0.00003346218,0.0008954026],"category_scores_gemma":[0.03499039,0.0001018608,0.0005362579,0.001105556,0.0001503686,0.00006190423,0.00007683277,0.001279816,0.000001050598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003732805,"about_ca_system_score_gemma":0.000189495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009275031,"about_ca_topic_score_gemma":4.12328e-7,"domain_scores_codex":[0.9966372,0.0005343776,0.0006749756,0.0001972765,0.001571799,0.0003843373],"domain_scores_gemma":[0.9902791,0.008055713,0.0008090873,0.0001836783,0.0003557623,0.0003166265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.03306143,0.003796452,0.4740769,0.002349296,0.01990055,0.001578651,0.04741841,0.002474608,0.0182831,0.00213404,0.0270813,0.3678452],"study_design_scores_gemma":[0.0469203,0.01295781,0.006241422,0.0003791363,0.003001671,0.003287953,0.0325539,0.004484817,0.0004318787,0.000139514,0.8893263,0.0002752789],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8049371,0.1498379,0.009087878,0.0284764,0.0006870189,0.003110949,0.0001265496,0.00007197427,0.003664232],"genre_scores_gemma":[0.9878985,0.006264299,0.002281233,0.0003821392,0.001321658,0.00009733959,0.00005336989,0.00004137828,0.001660058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.862245,"threshold_uncertainty_score":0.9804031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05182683679887252,"score_gpt":0.3386559417724136,"score_spread":0.2868291049735411,"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."}}