{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004216827,0.001142346,0.001622711,0.003717641,0.0006199243,0.001819369,0.00164272,0.001309608,0.001455159],"category_scores_gemma":[0.01200197,0.0004175798,0.001715502,0.002373448,0.0007331584,0.001161266,0.001019644,0.002530322,0.0003668563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007120849,"about_ca_system_score_gemma":0.001098883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01190084,"about_ca_topic_score_gemma":0.007903286,"domain_scores_codex":[0.9980319,0.001167353,0.0001313232,0.0004097324,0.0001481424,0.0001115136],"domain_scores_gemma":[0.9897391,0.008876533,0.0004560043,0.0004070255,0.0003575149,0.0001637689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005450629,0.0008232446,0.4237341,0.0004776952,0.002163765,0.001754199,0.000645248,0.2747953,0.002034405,0.009013429,0.006379165,0.2776344],"study_design_scores_gemma":[0.00004760753,0.0001554549,0.04186776,0.0001339812,0.0002715065,0.0003362107,0.0002232036,0.9258114,0.0002686763,0.02883077,0.002005071,0.00004836508],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4412531,0.01454411,0.5209595,0.01593522,0.0004552171,0.0002272813,0.003249874,0.0007258513,0.002649877],"genre_scores_gemma":[0.9350399,0.001957608,0.05820002,0.0009680819,0.0006919971,0.0001221604,0.001971153,0.00004659822,0.001002438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01190084,"threshold_uncertainty_score":0.02366316,"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."}}