{"id":"W4390538848","doi":"10.3390/medicina60010089","title":"From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization","year":2024,"lang":"en","type":"review","venue":"Medicina","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Computer science; Artificial intelligence; Chromoendoscopy; Virtual colonoscopy; Characterization (materials science); Colorectal Polyp; Colonoscopy; Medicine; Medical physics; Colorectal cancer; Nanotechnology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005951441,0.0003661129,0.002093731,0.0004723143,0.00003370571,0.00001587886,0.000144449,0.0002813045,0.0002425967],"category_scores_gemma":[0.0005538377,0.0002693866,0.0003930514,0.001209658,0.00006251567,0.00003515837,0.00009533345,0.0005867523,0.00009187884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002857815,"about_ca_system_score_gemma":0.0005235978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001540404,"about_ca_topic_score_gemma":0.000007706087,"domain_scores_codex":[0.9975792,0.0001083691,0.001069438,0.000509985,0.0005088267,0.0002242454],"domain_scores_gemma":[0.9988793,0.0001094262,0.0003430437,0.0003348152,0.0001586264,0.0001747904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001429923,0.00002216842,2.79073e-7,0.07666832,0.0001051846,0.00003419688,0.0002555677,4.033948e-9,0.00005888592,0.00001717895,0.0006415434,0.9220537],"study_design_scores_gemma":[0.00001695995,0.001590292,0.000002158648,0.3889019,0.002361197,0.00006599185,0.00005841304,0.00001201946,0.0004072396,0.00004600356,0.6063625,0.0001753207],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005310587,0.9925627,0.003223058,0.0004679494,0.0008430438,0.001828171,0.0001174191,0.0002452024,0.0006593344],"genre_scores_gemma":[0.0001324868,0.9962568,0.0006740086,0.0003717164,0.001415225,0.000233761,0.0007005865,0.00006822418,0.0001471654],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9218783,"threshold_uncertainty_score":0.9999759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06542235475775623,"score_gpt":0.3954455597071148,"score_spread":0.3300232049493586,"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."}}