{"id":"W4414768645","doi":"10.3390/cancers17193218","title":"Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer","year":2025,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia; Spinal Cord Injury BC","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Radiomics; Cervical cancer; Lymph node; Feature selection; Generalizability theory; Cancer; Nomogram; Feature (linguistics)","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":[0.002277381,0.0006128057,0.0004873716,0.001004612,0.0002220208,0.0005961293,0.0005126973,0.0004183245,0.00137165],"category_scores_gemma":[0.00358756,0.0001871552,0.0009233327,0.0004777473,0.0001788246,0.0002951304,0.0003939688,0.0005823986,0.000476871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005482555,"about_ca_system_score_gemma":0.0005188277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266111,"about_ca_topic_score_gemma":0.002743125,"domain_scores_codex":[0.9995968,0.00016978,0.00003169656,0.0001007558,0.00005424698,0.00004675971],"domain_scores_gemma":[0.9987817,0.0007160616,0.0001769742,0.0001014243,0.0001562705,0.00006760402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001414479,0.0001998475,0.8726698,0.0001328584,0.0003464145,0.00008926055,0.00006317104,0.03148784,0.002825008,0.00007906591,0.001342194,0.08935012],"study_design_scores_gemma":[0.00009259835,0.001046987,0.536625,0.0001039161,0.0006567778,0.0005890831,0.0001100703,0.4528044,0.00477847,0.0007658327,0.002387916,0.00003899616],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859117,0.00187708,0.009494191,0.0002728843,0.00003155367,0.0000602725,0.001363857,0.0002625714,0.0007259447],"genre_scores_gemma":[0.9956591,0.0001569573,0.002797065,0.00002830967,0.00001486424,0.00003334601,0.001132964,0.00001256467,0.0001648178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002277381,"threshold_uncertainty_score":0.01204407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283471922920056,"score_gpt":0.3101670330023721,"score_spread":0.2973323137731715,"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."}}