{"id":"W4416198715","doi":"10.1158/2643-3230.bcd-25-0408","title":"The Global State of Blood Cancers: An Ongoing Challenge","year":2025,"lang":"en","type":"article","venue":"Blood Cancer Discovery","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"State (computer science); Cancer; Field (mathematics); Toll; Cancer treatment","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":[0.0002437347,0.0002057792,0.0003668211,0.00004486918,0.0001839883,0.00008260581,0.0003001924,0.00006332147,0.00001907507],"category_scores_gemma":[0.00005387158,0.0001503287,0.0001495666,0.000453689,0.000175793,0.000391472,0.0001049734,0.0001860485,0.000001807374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002411631,"about_ca_system_score_gemma":0.001485259,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00749364,"about_ca_topic_score_gemma":0.0130496,"domain_scores_codex":[0.9982847,0.00004804316,0.0003964718,0.0003788842,0.0004437595,0.0004481186],"domain_scores_gemma":[0.9989466,0.00005722869,0.0001593857,0.0005592096,0.0001589522,0.0001185996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004585519,0.001493433,0.6201393,0.00158537,0.006241883,0.0003772537,0.002036939,0.00211488,0.007222752,0.02604737,0.004819998,0.3233353],"study_design_scores_gemma":[0.03883526,0.009359932,0.6352735,0.01811215,0.01777094,0.0002937829,0.01285269,0.001528874,0.1310757,0.03432254,0.09742186,0.003152804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9432356,0.04079447,0.0001312799,0.001839875,0.0005199559,0.0003770589,0.0001495689,0.00005531491,0.01289689],"genre_scores_gemma":[0.9910355,0.00658528,0.00009905607,0.0008265486,0.0002210446,0.00008781005,0.000008253748,0.00001373849,0.001122816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3201825,"threshold_uncertainty_score":0.9991155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03640625996574852,"score_gpt":0.345158677566221,"score_spread":0.3087524176004725,"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."}}