{"id":"W4388929818","doi":"10.1117/1.jbo.29.5.052915","title":"Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging","year":2023,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Optical Polarization and Ellipsometry","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia; McMaster University; University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University Health Network","keywords":"Medicine; Colorectal cancer; Stage (stratigraphy); Adjuvant therapy; Cohort; Artificial intelligence; Radiology; Cancer; Computer science; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005472457,0.0001210367,0.0002421461,0.0006474922,0.00005079029,0.00003589654,0.0001235308,0.0001183296,0.00007037177],"category_scores_gemma":[0.0002726949,0.0001084601,0.00007391103,0.001401411,0.00008355408,0.0001669622,0.00002600813,0.0007331631,0.00001598797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002110467,"about_ca_system_score_gemma":0.0001031799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001091387,"about_ca_topic_score_gemma":0.000002482446,"domain_scores_codex":[0.9985809,0.00005021135,0.0005535939,0.00009155897,0.0004384764,0.0002852016],"domain_scores_gemma":[0.9994403,0.0001115473,0.00009618909,0.00004534859,0.00008311242,0.0002235544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002063731,0.0002973758,0.07166699,0.0003627476,0.0001448935,0.0005301967,0.0004843204,0.5796576,0.301802,0.0001048498,0.001048928,0.04369368],"study_design_scores_gemma":[0.0008328323,0.0000678326,0.002401948,0.0002039957,0.00002478314,0.00002825916,0.00006413821,0.9928307,0.001679136,0.00003380851,0.001727039,0.0001055517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4277576,0.001419211,0.5645254,0.001763284,0.003790865,0.0001411,0.0000426731,0.0002728729,0.0002869765],"genre_scores_gemma":[0.9963406,0.0002404452,0.003038949,0.00004852524,0.0002750824,9.381006e-7,0.0000137945,0.00002689595,0.00001478005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.568583,"threshold_uncertainty_score":0.4422876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085676827674435,"score_gpt":0.2535174432112383,"score_spread":0.2426606749344939,"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."}}