{"id":"W2799976003","doi":"10.1007/s12149-018-1260-1","title":"18F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin’s lymphoma as predictors of treatment outcome and survival","year":2018,"lang":"en","type":"article","venue":"Annals of Nuclear Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; Women's College Hospital; University of Toronto; University Health Network; Mount Sinai Hospital","funders":"","keywords":"Medicine; Lymphoma; Positron emission tomography; Internal medicine; Retrospective cohort study; Aggressive lymphoma; Oncology; Radiomics; Nuclear medicine; Radiology; Rituximab","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":[],"consensus_categories":[],"category_scores_codex":[0.0006626601,0.0002881005,0.001185768,0.0003786381,0.00005920113,0.00000963166,0.0001267756,0.00002640165,0.00006224895],"category_scores_gemma":[0.001331589,0.0002021502,0.00009774695,0.0002465228,0.001288756,0.00007344856,0.00007066658,0.0001973916,0.000003093799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003145175,"about_ca_system_score_gemma":0.00007991213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001841836,"about_ca_topic_score_gemma":0.00002333874,"domain_scores_codex":[0.9981523,0.0001037278,0.0006369844,0.0003812681,0.0003756879,0.0003500672],"domain_scores_gemma":[0.9984809,0.0002635689,0.0004418979,0.0003312205,0.0001353348,0.0003470512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004043279,0.001174031,0.8196195,0.0010303,0.00216346,0.00536897,0.01069211,0.00002381364,0.01438414,0.002384017,0.01135072,0.1277656],"study_design_scores_gemma":[0.01324978,0.008118285,0.9448849,0.001677837,0.0006637047,0.004078856,0.002199127,0.003354104,0.001968802,0.0003270755,0.01908928,0.0003882762],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882485,0.002517973,0.000003060017,0.007157685,0.0003296043,0.0003323346,0.000009613226,0.00002455732,0.001376672],"genre_scores_gemma":[0.9929383,0.003614076,0.0006993801,0.002310742,0.000259521,0.00000489055,0.00001075585,0.00005023592,0.0001121366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1273773,"threshold_uncertainty_score":0.8243446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02597001224701781,"score_gpt":0.3372354359597715,"score_spread":0.3112654237127537,"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."}}