{"id":"W4220761695","doi":"10.1002/jha2.421","title":"A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA","year":2022,"lang":"en","type":"article","venue":"eJHaem","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Spinal Cord Injury BC; University of British Columbia; BC Cancer Agency","funders":"F. Hoffmann-La Roche","keywords":"Texture (cosmology); Computer science; Image (mathematics); Artificial intelligence","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":[0.002068687,0.001006708,0.001030121,0.001598402,0.0003928671,0.0009514467,0.0007805813,0.0005501267,0.0008008002],"category_scores_gemma":[0.003543963,0.0003180271,0.001437977,0.0006561959,0.0003043201,0.0004300538,0.0005655713,0.0006875562,0.0003358372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009684017,"about_ca_system_score_gemma":0.001473615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01302084,"about_ca_topic_score_gemma":0.009661087,"domain_scores_codex":[0.9995639,0.0001350129,0.00002734485,0.0001223143,0.00008634628,0.00006510286],"domain_scores_gemma":[0.9992421,0.0003826086,0.0001382506,0.00004114301,0.0001416643,0.00005432856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001326472,0.0003638069,0.3146171,0.0001484814,0.0009310714,0.000581144,0.0001240069,0.5222825,0.00589469,0.001746372,0.003984251,0.148],"study_design_scores_gemma":[0.00006945541,0.0002435252,0.01866917,0.00002021248,0.0001803673,0.000364278,0.00001625541,0.9776894,0.0004948072,0.001494803,0.0007237809,0.00003399852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6333849,0.001251273,0.3579818,0.001260096,0.0001180634,0.000440412,0.002685808,0.001210854,0.001666723],"genre_scores_gemma":[0.9611931,0.0003018366,0.03522266,0.0001266233,0.00005654648,0.0002564964,0.001985522,0.00004344505,0.0008138641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01302084,"threshold_uncertainty_score":0.02589011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04346131077421968,"score_gpt":0.3716677394525217,"score_spread":0.328206428678302,"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."}}