{"id":"W2897533498","doi":"10.4103/jpi.jpi_52_18","title":"Validation of Remote Digital Frozen Sections for Cancer and Transplant Intraoperative Services","year":2018,"lang":"en","type":"article","venue":"Journal of Pathology Informatics","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Hospital Foundation","funders":"","keywords":"Concordance; Medicine; Medical diagnosis; Kappa; Gold standard (test); Cancer; Cancer detection; Radiology; Medical physics; Nuclear medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01280061,0.000428296,0.0001841644,0.0009861425,0.0004148301,0.0006215827,0.000849771,0.0004133886,0.004336555],"category_scores_gemma":[0.01642448,0.0002028204,0.0003748046,0.0004640942,0.0008279628,0.0006785124,0.001221675,0.0002616953,0.0008479402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005744954,"about_ca_system_score_gemma":0.0007075402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001736492,"about_ca_topic_score_gemma":0.002031263,"domain_scores_codex":[0.9942767,0.002436811,0.000640277,0.001021224,0.001378962,0.0002460459],"domain_scores_gemma":[0.9897432,0.004224881,0.001870874,0.001196045,0.002509067,0.0004558742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00106328,0.0002236632,0.915129,0.0003877792,0.00005321233,0.0003021718,0.001798065,0.0006335331,0.03017432,0.000209259,0.0004520168,0.04957375],"study_design_scores_gemma":[0.0001276265,0.004291374,0.9082028,0.0003419377,0.0001554126,0.003812927,0.002937153,0.007145577,0.0653734,0.0003311752,0.007221483,0.00005917987],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882627,0.0003436103,0.007364827,0.00008259699,0.0000301808,0.000407388,0.0002928161,0.0001361009,0.003079759],"genre_scores_gemma":[0.9858295,0.0001071015,0.01294786,0.00005464397,0.00001376306,0.0001662636,0.0004612278,0.00002297322,0.0003966492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01280061,"threshold_uncertainty_score":0.06769693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02509946173244509,"score_gpt":0.3205129440496486,"score_spread":0.2954134823172035,"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."}}