{"id":"W4406087248","doi":"10.1158/2159-8290.cd-24-0760","title":"The Hallmarks of Predictive Oncology","year":2025,"lang":"en","type":"article","venue":"Cancer Discovery","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Vector Institute; Public Health Ontario; University Health Network; University of Toronto; Université de Montréal; Simon Fraser University; Princess Margaret Cancer Centre","funders":"Moonshot Research and Development Program; Argonne National Laboratory; Frederick National Laboratory for Cancer Research; National Cancer Institute; National Institutes of Health; National Institute of General Medical Sciences; Schmidt Family Foundation; American Cancer Society; Cancer Moonshot; U.S. Department of Energy","keywords":"Interpretability; Benchmarking; Generalizability theory; Computer science; Standardization; Relevance (law); Set (abstract data type); Precision medicine; Precision oncology; Personalized medicine; Data science; Medical physics; Artificial intelligence; Medicine; Psychology; Bioinformatics; Biology; Pathology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2208449,0.00113155,0.002132868,0.005688746,0.002965881,0.02043245,0.005271194,0.004563561,0.002453326],"category_scores_gemma":[0.3370169,0.000811934,0.00188663,0.003765305,0.03674132,0.0181261,0.01315932,0.01390276,0.0009222215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007658747,"about_ca_system_score_gemma":0.01309113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003291925,"about_ca_topic_score_gemma":0.002119829,"domain_scores_codex":[0.7983266,0.1282928,0.01486726,0.009063179,0.04712094,0.002329255],"domain_scores_gemma":[0.6060219,0.224252,0.02674729,0.08467458,0.05349642,0.004807735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001255977,0.0000693989,0.004601308,0.0004688287,0.0001405416,0.00007182283,0.001113388,0.007404474,0.0004641161,0.9073321,0.01227427,0.06593417],"study_design_scores_gemma":[0.00003866148,0.0001203707,0.001392852,0.0007697114,0.00005894807,0.0001441611,0.0003238451,0.0141379,0.001146914,0.9544658,0.02731888,0.00008187399],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02438156,0.01162146,0.7183715,0.1864662,0.002920738,0.0005936877,0.0006604156,0.001417428,0.05356695],"genre_scores_gemma":[0.6859547,0.003798131,0.2851066,0.01735842,0.00251028,0.001119395,0.0005217119,0.0005220792,0.003108669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2208449,"threshold_uncertainty_score":0.9608369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007514707090446018,"score_gpt":0.335857931246317,"score_spread":0.328343224155871,"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."}}