{"id":"W2996583486","doi":"10.1136/jclinpath-2019-206155","title":"Current opinion, status and future development of digital pathology in Switzerland","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Pathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Current (fluid); Pathology; Medicine; Data science; Bioinformatics; Computer science; Biology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005604822,0.0002912518,0.0002216745,0.001947004,0.0006317885,0.003693629,0.0008820452,0.001608619,0.0116211],"category_scores_gemma":[0.01072814,0.0001642893,0.00046293,0.002083991,0.001820607,0.003069322,0.001454154,0.0008902568,0.00122479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003277107,"about_ca_system_score_gemma":0.006283619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009069297,"about_ca_topic_score_gemma":0.008560131,"domain_scores_codex":[0.9964468,0.001600423,0.0002582541,0.0003013629,0.0008108392,0.0005823649],"domain_scores_gemma":[0.9856307,0.006261528,0.002405587,0.0002708426,0.00255198,0.002879365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00024718,0.00008717574,0.08153283,0.009998196,0.0000777208,0.001682671,0.006297078,0.0005925424,0.001066852,0.0125018,0.06823573,0.8176802],"study_design_scores_gemma":[0.00005815703,0.0005683757,0.2256004,0.02834746,0.000197052,0.009126414,0.03915305,0.001222692,0.001456995,0.005981482,0.688152,0.0001359367],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.08090852,0.6865662,0.001478904,0.1940133,0.003207513,0.00003615131,0.0007733207,0.000173747,0.03284236],"genre_scores_gemma":[0.5897592,0.390271,0.001732587,0.01065673,0.002486221,0.00005684296,0.0009277564,0.00004966307,0.00405999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0116211,"threshold_uncertainty_score":0.03887641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04366967984587516,"score_gpt":0.3758514600080569,"score_spread":0.3321817801621818,"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."}}