{"id":"W2999944568","doi":"10.1186/s13104-019-4866-z","title":"CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology","year":2020,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"Nvidia","keywords":"Grading (engineering); Breast cancer; Medicine; Concordance; Medical physics; Workload; Intraclass correlation; Quality assurance; Cohen's kappa; Pathology; Computer science; Cancer; Machine learning; Internal medicine; External quality assessment","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":[0.001007126,0.0001723594,0.0002271433,0.0002593154,0.000331879,0.0003510085,0.0007048782,0.00009115189,0.0000606244],"category_scores_gemma":[0.00109868,0.0001739124,0.00002737448,0.001385368,0.0002593799,0.0008167452,0.0008500489,0.0006745005,0.00000820375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008140296,"about_ca_system_score_gemma":0.0004434538,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008319015,"about_ca_topic_score_gemma":0.002697113,"domain_scores_codex":[0.9973449,0.0002734802,0.0002982687,0.0008667078,0.0005237937,0.0006929041],"domain_scores_gemma":[0.9982644,0.0009087792,0.0001102525,0.0003198021,0.000174315,0.0002224103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006154837,0.00003114086,0.9236996,0.000372756,0.00000891671,0.0001126955,0.007139361,0.001402311,0.04449362,0.003357693,0.001025005,0.01829531],"study_design_scores_gemma":[0.001042482,0.0002336139,0.1952126,0.0004784701,0.000006513483,0.00007510577,0.0007314196,0.788895,0.004684304,0.007802455,0.0002824263,0.0005556869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4904666,0.002664806,0.4752533,0.02752047,0.0006607334,0.001007246,0.00005858325,0.0006217941,0.001746483],"genre_scores_gemma":[0.9741341,0.0001292208,0.02483766,0.0003541441,0.0003179176,0.0001564887,8.035834e-7,0.00003177983,0.00003789498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7874926,"threshold_uncertainty_score":0.9982847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3774720781543485,"score_gpt":0.4231396925012519,"score_spread":0.04566761434690347,"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."}}