{"id":"W4408612078","doi":"10.2196/67840","title":"Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cervical cancer; Medicine; Population; Cancer prevention; Medical physics; Machine learning; Cancer; Computer science; Artificial intelligence; Environmental health; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02289508,0.0009988643,0.0005909089,0.001070768,0.000900511,0.001056705,0.001597955,0.001151595,0.001106976],"category_scores_gemma":[0.04852353,0.0004256952,0.001736276,0.0007781568,0.001388353,0.0007532684,0.001955865,0.001728915,0.0004648385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000802896,"about_ca_system_score_gemma":0.001828248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002832225,"about_ca_topic_score_gemma":0.002582771,"domain_scores_codex":[0.99225,0.004846983,0.0003455407,0.001548738,0.0007405819,0.0002682152],"domain_scores_gemma":[0.9673318,0.0146991,0.003024694,0.01041287,0.00370748,0.0008240154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001448983,0.001177463,0.9739392,0.00008494863,0.001173862,0.0001622353,0.0005021893,0.004671634,0.001299833,0.0004590933,0.001335498,0.01374504],"study_design_scores_gemma":[0.0008572952,0.004015588,0.8767338,0.000150093,0.001948878,0.001642156,0.0009098781,0.09828541,0.006714702,0.003111313,0.00551436,0.0001163456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847369,0.0001532417,0.01245381,0.0001604402,0.00002472729,0.000416094,0.001359653,0.00009124974,0.0006039009],"genre_scores_gemma":[0.9895841,0.00004718041,0.006514987,0.0001197285,0.00002037208,0.0003233517,0.003200098,0.00002686859,0.0001631785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02289508,"threshold_uncertainty_score":0.1210822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1384443403218932,"score_gpt":0.4757389196828442,"score_spread":0.337294579360951,"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."}}