{"id":"W4411789752","doi":"10.2196/69057","title":"Machine Learning for Preoperative Assessment and Postoperative Prediction in Cervical Cancer: Multicenter Retrospective Model Integrating MRI and Clinicopathological Data","year":2025,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Endometrial and Cervical Cancer Treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Cervical cancer; Medicine; Retrospective cohort study; Medical physics; Surgery; Cancer; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002658817,0.0002048266,0.0004757927,0.0001136356,0.0001391719,0.0000412073,0.00008015688,0.0001300456,0.00007477844],"category_scores_gemma":[0.0001609372,0.0001442331,0.00003917857,0.0002487678,0.0000943608,0.0001864451,0.0002056904,0.0004950953,3.230775e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005532994,"about_ca_system_score_gemma":0.00022382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005610237,"about_ca_topic_score_gemma":0.001089037,"domain_scores_codex":[0.9984955,0.00009725164,0.0003476728,0.00067373,0.00015643,0.0002293999],"domain_scores_gemma":[0.9993193,0.0001760211,0.00007848408,0.0001834687,0.000150257,0.00009253214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001506668,0.0002408176,0.9711719,0.0001179944,0.00008231206,0.000006464281,0.0007864049,0.0003275116,0.0003155768,0.0002988763,0.0001716186,0.02497384],"study_design_scores_gemma":[0.007311906,0.0006814484,0.5664971,0.0003953188,0.0001441079,0.000002149259,0.000355357,0.4238611,0.00008115952,0.0002309488,0.0003295138,0.0001098884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743839,0.006888746,0.004793525,0.006891524,0.0003213163,0.003891864,0.001208327,0.00008223693,0.001538552],"genre_scores_gemma":[0.9907331,0.002399299,0.003348278,0.0009872583,0.0001066366,0.001484608,0.0001824536,0.00001523835,0.0007430963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4235336,"threshold_uncertainty_score":0.5881655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05366085090039593,"score_gpt":0.4234147965723873,"score_spread":0.3697539456719914,"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."}}