{"id":"W4392966801","doi":"10.1101/2024.03.15.24303032","title":"New implementation of data standards for AI in oncology. Experience from the EuCanImage project","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación; Euskal Herriko Unibertsitatea; Eusko Jaurlaritza; Generalitat de Catalunya; European Commission; Centres de Recerca de Catalunya","keywords":"Precision oncology; Medical physics; Computer science; Data science; Oncology; Medicine; Internal medicine; Cancer","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":[0.001529358,0.0001623032,0.0004255775,0.00009747162,0.00002823831,0.00003990677,0.0005832978,0.0001137519,0.0001317674],"category_scores_gemma":[0.0007534488,0.0001084549,0.00007656316,0.0001419254,0.00009161478,0.00004550206,0.0010209,0.0009797174,0.000001441227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001821822,"about_ca_system_score_gemma":0.0030531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008260649,"about_ca_topic_score_gemma":0.0007423119,"domain_scores_codex":[0.9982816,0.00009129071,0.0004785564,0.000535837,0.0004146841,0.0001980268],"domain_scores_gemma":[0.9985948,0.0003063098,0.000165382,0.0007844089,0.00008433061,0.00006472322],"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":[0.0004107805,0.000112766,0.1169073,0.001217491,0.000363454,0.000134951,0.0271735,0.00001850867,0.01059715,0.0004306836,0.1990052,0.6436283],"study_design_scores_gemma":[0.005198399,0.0005871847,0.1006612,0.002901298,0.0008872258,0.00002732756,0.008564022,0.038968,0.003103968,0.00835955,0.8302279,0.0005139899],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9331284,0.002483509,0.0248123,0.0339283,0.001333846,0.002243936,0.001418811,0.00006123322,0.000589647],"genre_scores_gemma":[0.9615417,0.0004410293,0.03344969,0.001807246,0.0009400967,0.0002007988,0.001318238,0.00006512494,0.0002361381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6431143,"threshold_uncertainty_score":0.9983434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06891323366201042,"score_gpt":0.483450062649563,"score_spread":0.4145368289875526,"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."}}