{"id":"W4376868913","doi":"10.1016/j.clon.2023.05.006","title":"Functional Magnetic Resonance Imaging in Cervical Cancer Diagnosis and Treatment","year":2023,"lang":"en","type":"review","venue":"Clinical Oncology","topic":"Endometrial and Cervical Cancer Treatments","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Institute for Health and Care Research","keywords":"Medicine; Magnetic resonance imaging; Cervical cancer; Chemoradiotherapy; Biomarker; Functional magnetic resonance imaging; Cancer; Radiology; Brachytherapy; Disease; Imaging biomarker; Oncology; Radiation therapy; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004585855,0.0004155522,0.00348664,0.0002731706,0.0000440747,0.00001148556,0.0000994062,0.0006229416,0.001368558],"category_scores_gemma":[0.0006750976,0.0002784871,0.0006438626,0.000691769,0.0002650151,0.00002684583,0.000137874,0.0006232507,0.0003433393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001270929,"about_ca_system_score_gemma":0.001219012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00056105,"about_ca_topic_score_gemma":0.0008750219,"domain_scores_codex":[0.9965354,0.0004376616,0.001499506,0.0008768095,0.0002134265,0.0004372234],"domain_scores_gemma":[0.9941377,0.004880198,0.0002573145,0.0002947918,0.0000516976,0.0003783176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002358962,0.0009200106,0.04104519,0.001074794,0.0001039863,0.0007220706,0.000006689001,4.188338e-8,5.404976e-9,0.0000271647,0.001580295,0.9542838],"study_design_scores_gemma":[0.005574342,0.002201785,0.09457218,0.00389101,0.001749053,0.00005108461,0.00000561078,0.00000371843,3.53507e-8,0.00007475744,0.8916848,0.0001916066],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001816836,0.9945242,3.258585e-7,0.001694654,0.001343855,0.0011857,0.0001110935,0.00006206135,0.0008963866],"genre_scores_gemma":[0.00002884838,0.9908257,0.00007209247,0.0007596443,0.001649931,0.004089708,0.0001140021,0.00006337476,0.00239666],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9540923,"threshold_uncertainty_score":0.9999667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2933295091726443,"score_gpt":0.5075848652234686,"score_spread":0.2142553560508243,"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."}}