{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008985812,0.001004379,0.001986856,0.002631574,0.000218612,0.001107883,0.0008584559,0.001196034,0.005467714],"category_scores_gemma":[0.002115893,0.0003977382,0.0007555492,0.002760991,0.0005238691,0.001003027,0.0006209305,0.00150966,0.001245323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008470151,"about_ca_system_score_gemma":0.002138666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004177032,"about_ca_topic_score_gemma":0.009397508,"domain_scores_codex":[0.9997597,0.00006477625,0.00005187188,0.0000430919,0.00006124535,0.00001925826],"domain_scores_gemma":[0.9992544,0.0004502976,0.0001387589,0.00001304541,0.0001161024,0.00002735464],"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":[0.0001106133,0.00004968087,0.0002042584,0.05039832,0.0003186495,0.00009020373,0.0000425301,0.0003275754,0.0003744411,0.001181506,0.02498527,0.921917],"study_design_scores_gemma":[0.000218846,0.0003413571,0.003271663,0.06931666,0.003241056,0.001615438,0.0001531686,0.0003250959,0.0004642824,0.002617402,0.9183649,0.00007012156],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000304325,0.9994138,0.00002629229,0.0001255711,0.00007183008,0.000002967444,0.00001056176,0.000002008424,0.0003164441],"genre_scores_gemma":[0.0004183378,0.9990251,0.00007328384,0.0002080726,0.0001073027,0.000004569533,0.00001415043,5.918998e-7,0.0001486817],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005467714,"threshold_uncertainty_score":0.01829129,"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."}}