{"id":"W2059242372","doi":"10.1016/j.humpath.2007.10.001","title":"Relationship between tumor grade and computed architectural complexity in breast cancer specimens","year":2008,"lang":"en","type":"article","venue":"Human Pathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Breast cancer; Cancer; Medicine; Oncology; Internal medicine","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":[0.0001547109,0.0001351271,0.0002225379,0.0002097517,0.0002797283,0.00002092791,0.0003108586,0.00006219568,0.00001583021],"category_scores_gemma":[0.000009328807,0.0001398857,0.00002938376,0.0002987854,0.0003784572,0.0001495458,0.000199051,0.0003384432,0.00001215025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001223633,"about_ca_system_score_gemma":0.00003675977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002167628,"about_ca_topic_score_gemma":0.0004166985,"domain_scores_codex":[0.9987108,0.0002077533,0.0002473771,0.0004251419,0.0001263294,0.0002826761],"domain_scores_gemma":[0.9993422,0.0001496636,0.0000991297,0.0003101753,0.00002827171,0.00007054811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004502396,0.00002023755,0.988677,0.000009255289,0.00000323987,0.0001538739,0.001276643,0.00007383954,0.0003658685,0.008491844,0.0001349421,0.0007887507],"study_design_scores_gemma":[0.000457729,0.00004930602,0.9797233,0.00001015047,0.000003069573,0.001538404,0.000005732688,0.001049417,0.00008134398,0.01689374,0.00004146507,0.0001462972],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814162,0.00006866587,0.01660231,0.001269017,0.0001830617,0.0001418355,0.00001536269,0.0001365344,0.0001670533],"genre_scores_gemma":[0.9935136,0.000001925717,0.006031214,0.0001957272,0.0001946028,0.00001860168,0.000005587741,0.000009410155,0.00002929162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01209748,"threshold_uncertainty_score":0.5704372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1373088066190314,"score_gpt":0.3194528294494821,"score_spread":0.1821440228304506,"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."}}